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The aim of this research is to implement a precise Wi-Fi indoor positioning system (IPS) or localization system based upon the IEEE 802.11mc Fine-Timing Measurement (FTM) scheme also known as the Wi-Fi Round Trip Time (RTT) ranging technique, where ranging refers to a sub-process of positioning that determines the distance between a transmitter and receiver. Our system and its algorithms were implemented using a COTS (Commercial-Off-The-Shelf) smartphone and Wi-Fi access points. Experiments were conducted in several real-life indoor environments. This paper presents the detailed Wi-Fi RTT ranging performance of these devices in different system configurations and characterizes the systematic biases and noise model to improve the ranging accuracy. A novel three-step-positioning method is proposed to overcome the issues of no or multiple intersect points in trilateration due to ranging errors to improve positioning accuracy. This consists of first, systematic bias determination and removal; second, Clustering-based Trilateration (CbT) supported by Weighted Concentric Circle Generation (WCCG), namely CbT & WCCG; third, positioning result and trajectory optimization using a Kalman filter. As a result, the evaluation experiments gave a position accuracy of ±1.2 m in 2D static positioning and ±1.3 m for dynamic motion tracking. Also, our CbT & WCCG method demonstrate…

Wireless communication systems play an essential role in everyday life situations and enable a wide range of location-based services to their users. The imminent adoption of 5G networks worldwide and the future establishment of next-generation wireless networks will allow various applications, such as autonomous vehicles, connected robotics, and most recently, crowd monitoring for fighting infectious diseases, such as COVID-19. In this context, radio localization techniques have become an essential tool to provide solid performance for mobile positioning systems, through increased accuracy or less computational time. With this in mind, we propose a trilateration-based approach using machine learning (ML) and sequential least-square programming (SLSQP) optimization to estimate the outdoor position of mobile terminals in cellular networks. The ML technique employed is the k -nearest neighbors ( k -NN). The optimization methods analyzed are Nelder–Mead (NM), genetic algorithms (GA), and SLSQP. Different environments (noise-free and noisy) and network scenarios (different numbers of base stations) are considered to evaluate the approaches. Numerical results indicate that the k -NN/SLSQP technique has similar accuracy compared to the k -NN/GA with eight generations. Both perform better than k -NN/NM in all scenarios and environments. When comparing computational times, our proposal is considerably more time-efficient. Aside from that, SLSQP computational time is less affected by network scenarios with more base stations in comparison with GA. That feature is significant considering the ultra-dense base station deployment forecasted for the next-generation cellular networks.

In this research, for an assembly-work guide, an ultrasonic positioning system is proposed to track the positions of an indoor moving object. Composed of an ultrasonic sender (moving object) and a receiver (anchor), the system employs three ultrasonic time-off-flights (TOFs) and trilateration to estimate the positions of the moving object in real time. In addition, to remove measuring noises, a pulse debounce technique and Kalman filter have been applied to the position estimation. Ultrasonic signals are processed in full digital with a Zynq SoC, in which a hardware/software co-design is implemented where digital circuit portion is designed in the Zynq’s fpga and software portion is c-coded in the Zynq’s dual processors by using the bare-metal multiprocessing. According to the test results, it was confirmed that circular motions could be tracked with accuracy of sub-centimeter and the location of a worker’s hand was well tracked.

Robot localization is one of the most fundamental problems in motion planing, with many important applications in tracking vehicles and router systems. One way for localizing the robot is trilateration, which is used in the environments without GPS antenna by using a number of signals that can be measure their distance to robot at any time. It has already been shown that $$\lfloor \frac{8n}{9}\rfloor $$ landmarks are sufficient for trilaterating a simple n-side. Later, as a better result, it has been proved that $$ \lfloor \frac{2n}{3}\rfloor $$ landmarks are sufficient for this purpose. In this paper, we discuss the problem for orthogonal polygons and show that $$\frac{n}{2}$$ landmarks suffice for trilaterating an orthogonal n-gon. This theoretical achievement is important for most evolutionary algorithms and neuro-computing techniques which need more speedup and high efficiency.

WeChat has earned more than one billion users worldwide. Research on the geolocation of WeChat users can not only discover the location of malicious users but also verify the validity of user privacy protection strategies. However, existing methods are susceptible to WeChat’s location confusion strategies, resulting in intolerable geolocating errors. In this article, a WeChat user geolocation method based on user missequence state analysis (MSAG) is proposed. Different from the existing methods which usually rely on the relationship between reported and actual distances of nearby users, MSAG utilizes the relation between user order and actual distances to geolocate the target. By statistical analysis of sequence changes of nearby users under different actual distances, the distance range that causes nearby users missequence is determined. During geolocation, the distance range of the target is delimited by checking the missequence state of the target and a user with a known location. Finally, we discuss trilateration strategies for different abnormal situations. Experimental results show that MASG can achieve high-precision geolocation of WeChat users, the average error is less than 50 m, and 72% of geolocating errors are within 60 m; compared with existing typical algorithms, the average error is reduced by 23.7%–50.7%.

The cognitive radio network (CRN) has been proposed to overcome the spectrum scarcity and the massive demand on the radio frequencies through efficient utilization and smart management of the free channels. Using a spectrum sensing process, the secondary users (SUs) share the spectrum of the primary users (PUs) without causing any interference to these latter. One of the main threats affecting the channel utilization in CRN is the primary user emulation (PUE) attack. The PUE attack utilizes a sophisticated technique to craft the same signal of a legitimate PU to gain unauthorized access to the unused channels, forcing the SUs to immediately free up vacant spectrum space, resulting in a denial of service (DoS), degradation of service, and causing a noticeable impact on CRN performance. To circumvent this attack, the anchor nodes must accurately estimate the coordinates of the PU signal source. In the literature, most of the localization of unknown signal source, in our case the PU/PUE, are based on the ranging schemes which measure the distance between the blind node and the anchors. Those anchors are aware of their location and situated in optimized positions to the signal source in order to permit an accurate PU/PUE position detection. The detection rate depends tightly on the distance separating the anchors to the signal source as well as the signal-to-noise ratio (SNR). In this paper, we demonstrate the impact of the distance on the detection error while highlighting the particle swarm optimization’s (PSO) advantage in optimizing the anchors positioning. Furthermore, we illustrate the SNR impact on the probability of detection, particularly in the situation of low SNR and the attacker in the vicinity to a real PU. The main contribution in this work is the proposition of an approach with the aim of protecting an area containing more than a single PU with a limited number of anchor nodes while providing a higher detection rate to stop any eventual PUE attack. This approach is based on a multi-objective particle swarm optimization (MOPSO) algorithm for PU/PUE position detection. It minimizes concurrently the probability of detection error related to the received signal strength (RSS)/trilateration and the SNR, with the main objective of finding all optimized positions for the mobile anchor nodes and obtains the most accurate PUE attack detection.

Visible Light Communication (VLC) is a novel optical wireless communication technology which uses Light Emitting Diodes (LEDs) and Photodiodes for coherent detection and very-high-data rate data communication system. The stringent Line of Sight (LoS) requirement in VLC makes it very suitable for Indoor Positioning System (IPS), to be used for autonomous and smart city infrastructure. The current work aims to implement a real time IPS system using VLC link in Network Simulator (NS-3). The VLC module is implemented by modelling real-time attributes of LEDs, optical channel, and the photodiodes. The localization is carried out using trilateration schemes which measures the Received Signal Strength Indication (RSSI) and the Time Difference of Arrival (TDoA) for position estimation of the target. The project is further extended to obtain a comparative analysis between VLC link and other existing technology, Wi-Fi, as far as positioning accuracy and other important performance metrics are concerned. The simulation results show significant improvement for the VLC link over the Wi-Fi link specially for the TDoA scheme accompanied by increasing number of beacon nodes.

One of the most popular technologies adopted for indoor localization is ultrawideband impulse radio (IR-UWB). Due to its peculiar characteristics, it is able to overcome the multipath effect that severely reduces the capability of receivers (sensors) to estimate the position of transmitters (tags) in complex environments. In this article, we introduce a new low-cost real-time locating system (RTLS) that does not require time synchronization among sensors and uses a one-way communication scheme to reduce the cost and complexity of tags. The system is able to evaluate the position of a large number of tags by computing the time difference of arrival (TDOA) of UWB pulse sequences received by at least three sensors. In the presented system, the tags transmit sequences of 2-ns UWB pulses with a carrier frequency of 7.25 GHz. Each sensor processes the received sequences with a two-step correlation analysis performed first on a field-programmable gate array (FPGA) chip and successively on an on-board processor. The result of the analysis is the time of arrival (TOA) of the tag sequence at each sensor and the ID of the associated tag. The results are sent to a host PC implementing trilateration algorithm based on the TDOA computed among sensors. We will describe the characteristics of the custom hardware that has been designed for this project (tag and sensor) as well as the processing steps implemented that allowed us to achieve an optimum localization accuracy of 10 cm.

The existing radio frequency-based positioning approaches widely used for indoor localization-based service (LBS) are fingerprinting and trilateration and their integration with the inertial sensors-based dead reckoning system. However, these localization methods have practical limits and challenges due to unstable signal strength, the cost of offline workload, computational complexity, terminal device heterogeneity, and accumulated sensor error. We propose a smartphone-based indoor localization system using weighted Spearman’s foot-rule (WSF)-based probabilistic fingerprinting for reliable smartphone localization service. This localization system adopts a real-time fingerprinting position error estimation approach realizing an adaptive extended Kalman filter (AEKF) to integrate the proposed fingerprinting localization with inertial measurement unit (IMU)-based localization. This proposed WSF-based smartphone localization uses a Gaussian process regression (GPR)-based signal prediction module to deal with fingerprinting localization’s offline workload. Furthermore, the smartphone localization system’s expected high computational complexity is controlled by employing a data-clustering module. The proposed WSF also employs a rank vector that helps mitigate the effect of terminal device heterogeneity. The proposed localization system is experimentally evaluated at two different representative indoor environments. Experimental results obtained by real field deployment show that the mean error is 2.06 m in an elongated hallway corridor and 3.47 m in the crowded and well-furnished wide area.

There are surplus applications in modern smart cities where localization of indoor environments is critical ranging from surveillance and trailing in smart structures to the localized wireless distribution of advertising content in shopping malls. These applications are only successful if a robust and cost-effective real-time system is developed for precise localization. Another aspect considered for indoor localization is power consumption. Recent wireless standards such as Bluetooth Low Energy (BLE) and LoRa consume less power which makes them a perfect candidate for indoor localization. This work aims to carry out an experimental evaluation which would help to decide which wireless standard i.e., Wi-Fi, Bluetooth Low Energy (BLE), and LoRa are most suitable for indoor localization. Experiments are carried out using trilateration in three multiple environments. RSSI is used to calculate the coordinates of a sensor node. Results obtained from the experiment show that Wi-Fi is most accurate with an average error of 0.54 m. LoRa is second most accurate with an average error of 0.62 m and BLE is the least accurate with an average error of 0.82 m. These results can be used to decide which wireless standard is best suited for indoor localization.

The scale of wireless technologies penetration in our daily lives, primarily triggered by the Internet-of-things (IoT)-based smart cities, is beaconing the possibilities of novel localization and tracking techniques. Recently, low-power wide-area network (LPWAN) technologies have emerged as a solution to offer scalable wireless connectivity for smart city applications. LoRa is one such technology that provides energy efficiency and wide-area coverage. This article explores the use of intelligent machine learning techniques, such as support vector machines, spline models, decision trees, and ensemble learning, for received signal strength indicator (RSSI)-based ranging in LoRa networks, on a training dataset collected in two different environments: indoors and outdoors. The suitable ranging model is then used to experimentally evaluate the accuracy of localization and tracking using trilateration in the studied environments. Later, we present the accuracy of LoRa-based positioning system (LPS) and compare it with the existing ZigBee, WiFi, and Bluetooth-based solutions. In the end, we discuss the challenges of satellite-independent tracking systems and propose future directions to improve accuracy and provide deployment feasibility., Comment: 7 pages, 5 figures, 1 table

This article investigates the effect of additional calibration modules (CMs) in a 3-D real-time localization system during the calibration of the anchor position. A quick calibration is desirable for new anchors that are positionally undetermined in the working coordinate system. Three localization methods were tested for the anchor calibration: multidimensional scaling, semidefinite programming, and iterative trilateration. First, the accuracy of the anchor localization was studied by simulating a change in the number of additional CMs and their positions. Second, tests on a real system with ultrawideband modules were performed to validate the improvements in the anchor calibration when using the additional CMs. The experimental results revealed an improvement in the anchor localization for all three methods, where the average positional error was improved by 0.01 m in the first scenario and 0.30 m in the second scenario. The MDS method had the best absolute performance, with an average positional error that was as much as two times less in comparison with the other two methods. This investigation demonstrates that the positional error can be successfully reduced by using additional CMs. The calibration of anchor positions in the working coordinate system using additional CMs resulted in a 3-D error of less than 0.32 m.

Accurate ranging and localization in non-line-of-sight (NLOS) conditions is an open research problem. In indoor environments, NLOS causes ranging and location errors due to reflections, refraction, and other propagation phenomena. This article proposes a method to mitigate ranging errors caused by through-the-wall (TTW) NLOS conditions. We develop a TTW ranging model that has less parameters than the conventional ranging model found in the literature and evaluate it using measurements from ultrawideband radios. The developed ranging model expresses the difference between NLOS and line-of-sight (LOS) ranges, i.e., bias, in terms of walls’ relative permittivity and thickness. NLOS ranges expressed with the proposed TTW ranging model are used as input to a trilateration algorithm. The proposed method is evaluated via simulations, and it is shown that localization using the simplified ranging model as input to the trilateration algorithm effectively mitigates NLOS ranging errors, resulting in location estimates that are close to LOS location estimates.

Location based service (LBS) is a popular issue in recent years, which can be applied widely. The most common one is providing the local information and the guide of the Point of Interesting (POI) to users, which means positioning is the necessary technique to put LBS into practice. In an outdoor scenario, the user’s position can be obtained relying on the Global Navigation Satellite System (GNSS), however, the signal of GNSS might be blocked in a building. So, many indoor positioning techniques are developed in the decades, which have the pros and cons respectively. This paper proposes an indoor positioning technique by integrating Pedestrian Dead Reckoning (PDR) with the image-based positioning method, which can decrease the cost significantly because it only needs a camera built-in the smartphone. In the first experiment, we verify the accuracy of positioning by the proposed method, that the mean error in the horizontal direction is about 0.25 meters. In the following experiment, comparing with the misclosure of PDR only and PDR integrated with the proposed method, it can decrease from 8.53% to 1.44%. The improvement is about 83%, therefore, this method is suitable for applying to indoor navigation.

In this paper, an experimental evaluation of received signal strength indicator (RSSI-based) localization methods in an indoor wireless network is studied. The major contributions of this work are twofold. First, the well-known and widely used min–max and trilateration methods are tested in the cases of without and with human movement effects. By this purpose, how RSSI data during human movements affect the accuracy of such methods and which method shows the best position estimation result, have been investigated. Second, we also design and develop a new RSSI filter to automatically reduce RSSI variation and the position estimation error caused by human movements. Experiments are carried out in a parking building. An LPC2103F microcontroller interfaced with a CC2500 RF transceiver as a low-cost, low power, 2.4 GHz radio module is used as a wireless node. Results demonstrate that without human movement effects, the performances by both localization methods are not much different. However, with human movement effects, the min–max method shows better accuracy than the trilateration method in handling the RSSI variation problem. The results also indicate that by applying the proposed RSSI filter, it can directly cope with the RSSI variation problem caused by humans. The localization error decreases by 69.87% for the case of the min–max method, and it decreases by 72.74% for the case of the trilateration method (the best case). Compared with the case of employing the moving average filter as the commonly used filter, the localization error only decreases by 18.67% and 12.99%, respectively.

Accurate localization from Cyber-Physical Systems (CPS) is a critical enabling technology for context-aware applications and control. As localization plays an increasingly safety-critical role, location systems must be able to identify and eliminate faulty measurements to prevent dangerously inaccurate localization. In this article, we consider the range-based localization problem and propose a method to detect coordinated adversarial corruption on anchor positions and distance measurements. Our algorithm, G ordian , rapidly finds attacks by identifying geometric inconsistencies at the graph level without requiring assumptions about hardware, ranging mechanisms, or cryptographic protocols. We give necessary conditions for which attack detection is guaranteed to be successful in the noiseless case, and we use that intuition to extend G ordian to the noisy case where fewer guarantees are possible. In simulations generated from real-world sensor noise, we empirically show that G ordian ’s trilateration counterexample generation procedure enables rapid attack detection even for combinatorially difficult problems.

Visible light communication (VLC)-based indoor localization approaches enjoy many advantages, such as utilizing ubiquitous lighting infrastructure, high location accuracy, and no interruption to RF-based devices. However, existing VLC-based localization methods lack a real-time backward channel from the device to landmarks and necessitate computation at the device, which make them unsuitable for real-time tracking of small IoT devices. In this paper, we propose and prototype RETRO, that establishes an almost zero-delay backward channel by retroreflection. RETRO localizes passive IoT devices without requiring computation and heavy sensing (e.g., camera) at the devices. Multiple photodiodes (i.e., landmarks) are mounted on any single unmodified light source to sense the retroreflected optical signal (i.e., location signature). We derive a closed-form expression, which is validated by experiments and ray tracing simulations, for the reflected optical power relative to the location and the orientation of the retroreflector. The expression is applied to a received signal strength indicator and trilateration based localization algorithm. Extensive experiments demonstrate centimeter-level location accuracy and single-digit angular error. For practicality concern, to mitigate the thickness problem of a single retroreflector, the capabilities of different retroreflector arrays are studied. The range of the localization system is theoretically evaluated for different light emission patterns.

Indoor positioning plays an important role in various location-based services (LBSs). In conventional systems, the process of constructing radio maps for positioning usually involves labor-intensive manual calibrations, which seriously limits the system’s scalability and adaptiveness. In this paper, we propose an efficient calibration-free method by leveraging on crowdsourced WiFi signal data that are captured passively through a WiFi sensing testbed. Since the ground truths of the crowdsourced data are unavailable, the radio maps cannot be directly constructed. In the proposed method, we adopt the multidimensional scaling (MDS) technique to compute the positions of the unlabeled data thereby generating radio maps. In order to enable MDS, we estimate the pairwise distances among the unlabeled data by using an improved trilateration method and a law of cosine (LoC)-based geometrical algorithm without online pairwise measurements. Experimental results show that the accuracy of the proposed method is higher than trilateration-based method and reasonably lower than that of calibration-based method. Meanwhile, the run time of the proposed method is shorter than previous optimization-based methods. The short run time allows the radio maps to be dynamically updated against the environmental variations.

Positioning using Wi-Fi received signal strength indication (RSSI) signals is an effective method for identifying the user positions in an indoor scenario. Wi-Fi RSSI signals in an autonomous system can be easily used for vehicle tracking in underground parking. In Wi-Fi RSSI signal based positioning, the positioning system estimates the signal strength of the access points (APs) to the receiver and identifies the user&rsquo, s indoor positions. The existing Wi-Fi RSSI based positioning systems use raw RSSI signals obtained from APs and estimate the user positions. These raw RSSI signals can easily fluctuate and be interfered with by the indoor channel conditions. This signal interference in the indoor channel condition reduces localization performance of these existing Wi-Fi RSSI signal based positioning systems. To enhance their performance and reduce the positioning error, we propose a hybrid deep learning model (HDLM) based indoor positioning system. The proposed HDLM based positioning system uses RSSI heat maps instead of raw RSSI signals from APs. This results in better localization performance for Wi-Fi RSSI signal based positioning systems. When compared to the existing Wi-Fi RSSI based positioning technologies such as fingerprint, trilateration, and Wi-Fi fusion approaches, the proposed approach achieves reasonably better positioning results for indoor localization. The experiment results show that a combination of convolutional neural network and long short-term memory network (CNN-LSTM) used in the proposed HDLM outperforms other deep learning models and gives a smaller localization error than conventional Wi-Fi RSSI signal based localization approaches. From the experiment result analysis, the proposed system can be easily implemented for autonomous applications.

High-precision indoor localisation is becoming a necessity with novel location-based services that are emerging around 5G. The deployment of high-precision indoor location technologies is usually costly due to the high density of reference points. In this work, we propose the opportunistic fusion of several different technologies, such as ultra-wide band (UWB) and WiFi fine-time measurement (FTM), in order to improve the performance of location. We also propose the use of fusion with cellular networks, such as LTE, to complement these technologies where the number of reference points is under-determined, increasing the availability of the location service. Maximum likelihood estimation (MLE) is presented to weight the different reference points to eliminate outliers, and several searching methods are presented and evaluated for the localisation algorithm. An experimental setup is used to validate the presented system, using UWB and WiFi FTM due to their incorporation in the latest flagship smartphones. It is shown that the use of multi-technology fusion in trilateration algorithm remarkably optimises the precise coverage area. In addition, it reduces the positioning error by over-determining the positioning problem. This technique reduces the costs of any network deployment oriented to location services, since a reduced number of reference points from each technology is required.

Photodiode (PD)-based Visible Light Positioning (VLP)-based localisation systems seem propitious for the low-cost tracking and route-configurable navigation of automated guided vehicles, found in warehouse settings. Delivering the required high accuracy, currently necessitates measuring and fitting the received power - distance relation. This paper shows that accurately modelling the PD receiver & x2019;s angular characteristics obsoletes this calibrating fit, while still providing accurate positioning estimates. A new responsivity model Square (SQ) is proposed, which is a function of the square of the incidence angle rather than its cosine. Both its aptitude in matching real-life propagation and its associated localisation accuracy are verified using two extensive measurement sets, each detailing the propagation of a PD moving across a 2D plane 3 m below a 4-LED plane. SQ is compared to the responsivity and calibration fit models available in the literature. In conjunction with model-based fingerprinting positioning, SQ outscores the Lambertian and generalised Lambertian model in terms of the 90(th) percentile root-mean-square error (rMSE) $p_{90}$ by 45.36 cm (83.1 & x0025;) and 0.84 cm (8.4 & x0025;) respectively for the non-Lambertian-like receiver. $SQ$ exhibits an equivalent performance as the generalised Lambertian model for the Lambertian-like photodiode. Accounting for the appropriate receiver model can also boost trilateration & x2019;s rMSE. A 50(th) percentile rMSE reduction of respectively 1.87 cm and 2.66 cm is found in the setup.

International audience; In this paper, we study the problem of euclidean distance matrix (EDM) recovery aiming to tackle the problem of received signal strength indicator sparsity and fluctuations in indoor environments for localization purposes. This problem is addressed under the constraints required by the internet of things communications ensuring low energy consumption and reduced online complexity compared to classical completion schemes. We propose EDM completion methods based on neural networks that allow an efficient distance recovery and denoising. A trilateration process is then applied to recovered distances to estimate the target's position. The performance of different deep neural networks (DNN) and convolutional neural networks schemes proposed for matrix reconstruction are evaluated in a simulated indoor environment, using a realistic propagation model, and compared with traditional completion method based on the adaptative moment estimation algorithm. Obtained results show the superiority of the proposed DNN based completion systems in terms of localization mean error and online complexity compared to the classical completion.

In this paper, we present a Bluetooth Low Energy (BLE) based indoor positioning system developed for monitoring the daily living pattern of old people (e.g. people living with dementia) or individuals with disabilities. The proposed sensing system is composed of multiple sensors that are installed in different locations in a home environment. The specific location of the user in the building has been pre-recorded into the proposed sensing system that captures the raw Received Signal Strength Indicator (RSSI) from the BLE beacon that is attached on the user. Two methods are proposed to determine the indoor location and the tracking of the users: a trilateration-based method and fingerprinting-based method. Experiments have been carried out in different home environments to verify the proposed system and methods. The results show that our system is able to accurately track the user location in home environments and can track the living patterns of the user which, in turn, may be used to infer the health status of the user. Our results also show that the positions of the BLE beacons on the user and different quality of BLE beacons do not affect the tracking accuracy.

The localization of sensor nodes is a significant issue in wireless sensor networks (WSNs) because many applications cannot provide services without geolocation data, especially during disaster management. In recent years, a promising unknown-nodes positioning method has been developed that localizes unknown nodes, employing a GPS-enabled mobile anchor node moving in the network, and broadcasting its location information periodically to assist localization. In contrast to most studies on path planning that assume infinite energy of the mobile anchor node, the anchor node in this study, consumes different amounts of energy during phases of startup, turning, and uniform motion considering the aftermath of disasters. To enable a trade-off between location accuracy and energy consumption, a path-planning algorithm combining a Localization algorithm with a Mobile Anchor node based on Trilateration (LMAT) and SCAN algorithm (SLMAT) is proposed. SLMAT ensures that each unknown node is covered by a regular triangle formed by beacons. Furthermore, the number of corners along the planned path is reduced to save the energy of the mobile anchor node. In addition, a series of experiments have been conducted to evaluate the performance of the SLMAT algorithm. Simulation results indicate that SLMAT outperforms SCAN, LMAT, HILBERT, and Z-curve in terms of localization accuracy and energy consumption.

Owing to the flexibility and low cost, cooperative Unmanned Aerial Vehicles(UAVs) have been attractive in multi-target positioning recently. Although it is popular and easy to accomplish, positioning based on trilateration method still faces challenges under scenarios with multiple UAVs. First, large accumulated errors will be brought if a single UAV is used to perform trilateration on same targets. Second, due to the mobility of targets, the time interval between UAVs performing twice successive distance measurement on one target cannot be long for positioning precision. Finally, the limited energy provided by onboard battery limits the time for UAVs to perform tasks. Once the energy used by some of the UAVs reaches limitation, the whole positioning mission will fail. Thus, to complete the mission of locating multiple targets, this paper is intended to minimize the maximum energy consumption among all UAVs. We formulate the problem, and decompose it into two subproblems, one of which plans the routes for UAV groups and the other plans the routes for UAVs in a group. To solve the first subproblem, a heuristic algorithm called adjusted genetic algorithm (AGA) is proposed to plan trajectories for all UAV groups under constraints on maximum energy consumption. To guarantee stable performance and reduce computation complexity, we propose an approximation algorithm, Tree Decomposition united with Christofides Algorithm (TDCA), and the approximation ratio is proved to be (3 * N'/(2 * (N' - 1))), where N' denotes the number of UAV groups. For the second subproblem, a two-step greedy heuristic algorithm is proposed to plan trajectories for UAVs in same groups. Extensive simulations show that compared to existing algorithms, the proposed algorithms can reduce up to 26.6% maximum and 26.3% average energy consumption.

Indoor wireless positioning using Bluetooth Low Energy (BLE) beacons have attracted considerable attention from industry and academia given the many advantages of this technology such as low power consumption, low cost, easy layout, high availability, and high precision. However, the indoor positioning accuracy always suffers from non-line of sight (NLOS) propagation, stemming from the frequently occurring instances of reflection, refraction, or scattering of BLE radio signals due to the complexity of indoor environments. This article proposes an integrity monitoring (IM) algorithm to detect and eliminate two gross errors simultaneously to solve the adverse effects caused by the NLOS. The logarithmic attenuation model based on the received signal strength indication (RSSI) of BLE realizes positioning by combining trilateration and Least Squares Based on the Taylor expansion (LSBT). Furthermore, the IM based on hypothesis testing is employed to improve the positioning quality andthe users will be alerted in time to avoid risk from positioning accuracy no longer meet user's requirement. The performance of the proposed IM algorithm has been extensively tested by conducting simulation and field experiments. The experimental results show that the IM algorithm significantly improved BLE positioning accuracy as well as the robustness of the positioning system. The 90% average error (1.9143m) in seven groups of single point experiments was reduced by 34.48% over the 90% average error (2.9143m) of the LSBT method after performing IM, and the maximum error during continuous positioning did not exceed 3m after performing IM, which were better than only using LSBT positioning.

In this study, theoretical limits in position estimation in Wireless Sensor Network (WSN) using a trilateration method are derived in the form of probability density functions (PDFs) and compared with practical limits obtained via received signal strength (RSS) measurements in an anechoic chamber. In particular, the PDF of the radius of error of an unknown node’s position has been derived as having a Nakagami-m distribution. The theoretical PDFs is validated via Kolmogorov–Smirnov hypothesis tests at 95% confidence level against the empirical results of position estimation in a 4 m × 4 m area using the RSS measured in an ideal environment of an anechoic chamber. High-resolution equipment is used in the RSS measurements to ensure the inaccuracies due to limited-capability WSN equipment can be quantified. A lower bound of positioning accuracy to be expected in real environments via the received signal strength method has been established to be in the range of 11 cm from the true position with 95% confidence level. Better results cannot be expected in the real environments with bigger variations than the variations observed in the anechoic chamber unless a more sophisticated algorithm, equipment with better resolution and/or more precise measurement methods are employed.

Ultrawideband (UWB) technology applied in indoor localization systems is able to offer centimeter-level error in position measurement and attracts increasing popularity in various solutions. Position estimation is calculated based on ranging measurements between mobile nodes and anchors at known locations. Most existing works employ UWB in positioning context and resort to nonline-of-sight optimization imposed by static indoor building layout. In tracking applications, the direct path of UWB propagation may be obstructed by humans (pedestrians), which leads to significant ranging errors. It is observed that the ranging errors vary with respect to the degree of obstruction imposed by the pedestrian. In this paper, a UWB ranging error model with respect to the human body shadowing effect is proposed and evaluated by extensive measurements and experiments. The error model is applied in a particle filter pedestrian tracking algorithm fusing heading information obtained from an iPhone 7 built-in gyroscope. The performance of the proposed model outperforms the state-of-the-art approaches and has achieved up to 41.91% reduction in mean 2-D position error compared to the trilateration-based method in a pedestrian tracking case study.

Owing to the widespread use of LED lighting and multipath-free propagation, light-to-camera communications (LCC) hold potential in the deployment of ubiquitous, robust, and accurate indoor positioning systems. However, existing LCC-based positioning systems require the presence of the luminaires in the image, which limits the light-to-camera distance and the range of orientation estimation. We propose LIPO, which uses a smartphone to measure the signal strengths of reflections from the user’s chest. We design a set of modulation, sampling and signal recovery mechanisms, which resolves the received signal strength (RSS) of each beacon. Given the RSS measurements, LIPO estimates the user’s 3D location and heading direction via a simple trilateration algorithm. Our design overcomes the light-to-camera distance limitation which exists in most photogrammetry-based positioning approaches and extends the range of orientation estimation to full 360∘. We implement LIPO and extensively evaluate its performance in a 72-m2 room and a corridor. Our experiments demonstrate that LIPO can achieve decimeter location error and around 6∘ orientation error.

Indoor positioning is an emerging technology with wide applications. Augmented and mixed reality need accurate and real-time positioning of user’s limbs or direction of sight in real time, and reference points within the real environment. Positioning is also required for mall navigation, elderly people movements monitoring, and many others contexts. Indoor positioning of mobile units (MUs) can be provided by multilateration techniques that compute the location of an MU starting from distance measurements between the MU and a set of beacons. In this paper, a nonlinear closed-form solution for the trilateration problem is employed to avoid heavy numerical iterative algorithms. The closed-form solution is allowed by a particular arrangement of the beacons, placed at the vertex of a square. A positioning system is presented where MUs, exploiting the light algorithm of the closed-form positioning, are able to autonomously and privately calculate their own positions. A prototype of the positioning system has been designed, realized, and characterized for an average $4 \times 4 \times 3\,\,\text {m}^{3}$ home or office room. It includes four beacons that emit a sequence of ultrasonic chirp signals, several MUs, and a master unit that provides time synchronization via ANT transceivers onboard each MU and the master unit. Thanks to the adopted closed form solution, each tiny battery-operated MU is able to carry out all the computations onboard, including analog and digital signal processing, beacon-MU distance estimation, and finally MU positioning at a rate of 2 Hz.

This study presents a smart-phone-based hybrid indoor localisation system (HILS), which integrates two different localisation approaches, i.e., pedestrian dead reckoning (PDR) and Wi-Fi-based localisation to compensate the limits of each other. In PDR, the localisation accuracy degrades continuously when the moving distance increases from the starting reference point. However, Wi-Fi-based trilateration method requires the Wi-Fi signals from at least three access points (APs). In this study, the authors propose a more efficient novel Heron-bilateration-based position determination (HBPD) technique, which requires the Wi-Fi signals from only two APs to locate the mobile device. The proposed HILS utilises the HBPD technique to reset the bias drift errors involved with inertial sensors, and compensate the unavailability of strong Wi-Fi signals from APs using the PDR method. In this work, an experimental testbed is developed to collect the Wi-Fi signals and evaluate the proposed method. The proposed HILS is also compared with five other benchmark methods for two different trajectories and the experimental results show that the mean localisation error is decreased up to 1.68 and 1.96 m, which indicate the improvement by 16.83 and 20.97% for both the trajectories, respectively, in comparison with the best performing method among the benchmark methods.

Indoor positioning has attracted much attention in recent years due to the trend of Internet of Things (IoT), which is capable of providing numerous applications such as personal tracking, vehicle locator, and Location-Based Service (LBS). To put LBS into practice, positioning and navigation are one of the necessary techniques. Using the smartphone to process indoor positioning also become more and more usual. The most common algorithm in the inertial navigation system is Pedestrian Dead Reckoning (PDR), utilizing sensors built-in the smartphones to conquer the strait of GNSS-denied environment. However, for the purpose of eliminating the error accumulated with time, PDR combining with other algorithms, for instance, updating some geospatial information steadily is a better way to solve this problem. Therefore, this research proposes the imaged based aided algorithm. Moreover, in this study, a novel Artificial Neural Networks (ANN) embedded the system is proposed. The self-designed georeferenced markers and the indoor floor plan will be produced by an Indoor Mobile Mapping System (IMMS) in advance. This research proposed using Cascade-Correlation neural Network (CCN) to estimate the distance between the marker and the smartphone camera. The accuracy using this method can achieve to 0.27 meter. As if at least three coordinates and the distance can be obtained simultaneously, the position of the user can be calculated by the trilateration method. From the experiment, the accuracy of the positioning is about 0.5 meter. This way seems to have the high potential to bring into play on the real-time indoor positioning.

Location-based services in wireless sensor networks demand precise information of locations of sensor nodes. Range-based localization, a problem formulated as a two-dimensional optimization problem, has been addressed in this paper as a multistage exercise using bio-inspired metaheuristics. A modified version of the shuffled frog leaping algorithm (MSFLA) has been developed for accurate sensor localization. The results of MSFLA have been compared with those of geometric trilateration, artificial bee colony and particle swarm optimization algorithms. Dependance of localization accuracies achieved by these algorithms on the environmental noise has been investigated. Simulation results show that MSFLA delivers the estimates of the locations over 30% more accurately than the geometric trilateration method does in noisy environments. However, they involve higher computational expenses. The MSFLA delivers the most accurate localization results; but, it requires the longest computational time.

With the advancements in the mobile devices having Bluetooth Low Energy (BLE) capability, the BLE based indoor target localization is the recent trend. The majority of indoor localization methods generally rely on traditional simple techniques such as trilateration or angulation. However significant localization errors are involved with these techniques due to highly nonlinear relationship between RSSI and distance because of issues such as NLOS, multipath propagation. The Generalized Regression Neural Network (GRNN) with a one pass learning capability, is well known for its ability to train quickly. This paper proposes an application of GRNN as an alternative to these traditional techniques to obtain first location estimates of moving person using a hybrid network of PSOC BLE nodes and smartphone, which are further refined using Kalman filtering (KF) framework. Two algorithms namely, GRNN + Kalman filter and GRNN + Unscented Kalman filter are proposed in this research work. The GRNN is trained with the RSSI values from PSOC BLE nodes at various locations and the corresponding actual 2-D locations of the given monitoring area. The real time experiments prove the efficacy of the proposed algorithms over the traditional approach in the context of NLOS, multipath propagation.

Indoor localization based on received signal strength (RSS) will result in a decreased precision after the environment changes. In this paper, we develop an adaptive wireless indoor localization system (ILS) for dynamic environments. The system consists of the following two components: an automated database updating process and a new fingerprinting algorithm called adaptive signal model fingerprinting (ASMF). In the ILS, a self-locating mobile robot is set up to continuously collect RSS measurement data within the localization space for autonomously updating the fingerprint database. ASMF is designed to reduce the time consumption and the amount of RSS data needed for updating the database. The fingerprint of the signal in ASMF is constructed by the position of the beacons and three signal models, which can be duly corrected based on the regression and optimization algorithm. Finally, we propose experiments for positioning targets in the static and dynamic environments and compare the results of the ASMF algorithm with traditional trilateration and k-nearest-neighbor fingerprinting algorithms. The experimental results demonstrate that the ASMF-based ILS provides much better performance in both static and dynamic environments; furthermore, the positioning accuracy can be actually maintained by the autonomous updated ASMF database.

The movement of humans in wireless networks is one of major effects leading to significant received signal strength indicator (RSSI) variation. Using fluctuated RSSI on estimating the target position in the RSSI-based indoor localization system can give large error and poor decision of the system. In this paper, how the human movement affects the accuracy of an implemented indoor target localization system is explored by experiments, and a proposed simple RSSI filtering solution as the guideline solution to directly handle such a research problem is also presented. For our purpose, firstly, the RSSI-based indoor target localization system, which consists of design communication operations for measuring the RSSI in the wireless network and selected well-known localization methods (i.e. the min-max and the trilateration methods) for estimating the target position, is implemented and tested. Secondly, selected well-known filtering methods (i.e. the moving average and the exponentially weighted moving average filters) and the span thresholding filter (i.e. the proposed solution) are applied for reducing the RSSI variation and the estimated position error caused by the human movement. Our experiments have been carried out in an indoor environment. An LPC2103F microcontroller interfacing with a 2.4 GHz CC2500 RF module is developed and employed as the wireless node. Experimental results reveal that the estimated position error determined by the min-max and the trilateration methods significantly increases during the human movement, and converts according to human movement patterns and numbers of movement people. Also, the results demonstrate that by applying the moving average filter with a high window size and the exponentially weighted moving average filter with an optimal weighting factor to raw RSSI data, the estimated position error is not much improved. In contrast, the span thresholding filter gives better results and can directly cope with the human movement problem. In average, the localization error and the standard deviation decrease 11.921% and 42.086% in the case of the min-max method, and they decrease 44.535% and 87.154% in the case of the trilateration method.

Bluetooth Low Energy (BLE) is one of the RF-based technologies that has been utilizing Received Signal Strength Indicators (RSSI) in indoor position location systems (IPS) for decades. Its recent signal stability and propagation distance improvement inspired us to conduct this project. Beacons and scanners used two Bluetooth specifications, BLE 5.0 and 4.2, for experimentations. The measurement paradigm consisted of three segments, RSSI–distance conversion, multi-beacon in-plane, and diverse directional measurement. The analysis methods applied to process the data for precise positioning included the Signal propagation model, Trilateration, Modification coefficient, and Kalman filter. As the experiment results showed, the positioning accuracy could reach 10 cm when the beacons and scanners were at the same horizontal plane in a less-noisy environment. Nevertheless, the positioning accuracy dropped to a meter-scale accuracy when the measurements were executed in a three-dimensional configuration and complex environment. According to the analysis results, the BLE wireless signal strength is susceptible to interference in the manufacturing environment but still workable on certain occasions. In addition, the Bluetooth 5.0 specifications seem more promising in bringing brightness to RTLS applications in the future, due to its higher signal stability and better performance in lower interference environments.

Wireless sensor networks (WSNs) increasingly penetrate our everyday life and are already employed in a wide range of application areas, such as habitat monitoring, precision agriculture, home automation, and logistics. Localization of sensor nodes in a network is a highly desirable capability in all these applications. The ability to precisely determine the position of nodes in sensor networks enables many new upcoming technologies such as robotics, automated driving, traffic monitoring, or inventory management. For all these applications, different requirements regarding accuracy, reliability, and speed of position estimation are posed. WSNs is a field with many optimization problems that have to be addressed. Optimization of power consumption of nodes in WSNs is the main problem that have to be addressed. WSN node has a limited power backup so this makes it a very critical issue. This paper formulates the concern on how WSNs can take advantage of the computational intelligent techniques using multi-objective particle swarm optimization (MOPSO), with an overall aim of concurrently minimizing localization time, energy consumption during localization, and maximizing the number of nodes fully localized. The localization method optimized the power consumption during a Trilateration-based localization (TBL) procedure, through the adjustment of sensor nodes' output power levels. Finally, a parameter study of the applied PSO variant for WSN localization is performed, leading to results that display up to 32% better algorithmic improvements than the baseline outcomes in the measured objectives.

If there is not coordinating data, the sensor parameter is of no value in the internet of things system. A promising approach to offer location parameter is to leverage a specific localization method. However, there exist some uncertain factors, which result in low accurate localization. Many research works are presented to improve localization accuracy or localization efficiency. As many of them did not consider the uncertainty effect and mechanism during localization computation, the improvement is not promising. For this problem, an enhanced trilateration localization is therefore presented to improve the localization accuracy, which is called improved trilateration localization through quality evaluating and adaptive selecting (IT-QEAS). We firstly assess the quality of distance measurement results. Then these anchor nodes with higher quality distance are optimizing selected to perform localization calculation. Lastly, high accuracy location results can be achieved. Experimental results of definite localization data demonstrate that the trilateration localization can be improved effectively.

The time of arrival (TOA) trilateration is one of the representative location detection technologies (LDT) that determines the true location of a mobile station (MS) using a unique intersection point of three circles based on three radii corresponding to distances between MS and base stations (BSs) and center coordinates of BSs. Since the distance between MS and BS is estimated by using the number of time delays, three circles based on the estimated radii are generally increased and they may not meet at a single point, resulting in the location estimation error. In order to compensate this estimation error and to improve estimation performance, we present two advanced TOA trilateration localization algorithms with detail mathematical expressions. The considered algorithms are the shortest distance algorithm, which calculates an average of three interior intersection points among an entire six intersection points from three intersecting circles, and the line intersection algorithm, which calculates an intersection point of three lines connecting two intersection points of two circles among the three circles, as the estimated location of the MS. In this paper, we present both algorithms with detailed mathematical expressions. The computer simulation results are provided to compare the location estimation performance of both algorithms. In addition, in this paper, mathematical analysis is provided to indicate the relation between the line intersection algorithm and the shortest distance algorithm. In this analysis, we verify that line equations based on the intersection points obtained from the shortest distance algorithm are identical to those obtained from the line intersection algorithm.

In this paper the problem of localizing submerged sensors is observed and introduces a new method to ordain the coordinates of those sensors which are situated in underwater and they are mobile and only one beacon node is considered for the system. Collecting data of appropriate coordinate of the sensors in underwater wireless sensor networks (UWSN) is essential because the root contains confined value except the knowledge of real data. There is used trilateration technique to detect the submerged sensors and beacon is considered in six different positions. In this proposed model three sensors have been used. To find out the coordinates of the sensor nodes Cayley-Menger determinant is used for the proposed model. Here three mobile sensors and one beacon node that made six different volume tetrahedron shapes by making it in same volume through projection to apply Cayley-Menger determinant. Through enumerating of coordinates of sensors nodes, simulation results make out the proposed mathematical models with minimal errors.

Source localization has many important applications, especially in tracking and navigation. Trilateration, triangulation, and multilateration are three widely-used techniques for localization depending on the available information. The main drawbacks of these methods are the requirements of a large number of anchors and an elaborately designed layout. To accomplish the localization task with minimal resources while maintaining reasonable accuracy, we propose a 3-D source localization method with a compact infrastructure (prototype realized with 4 anchors located within an area of 2.5×20 cm2) by utilizing only phase-difference information. The proposed method first estimates the direction-of-arrival (DOA) of the target and then finds the candidate 3-D location along on the DOA by minimizing a cost function. This system is compared to the other two similar setups based on simulations and the experimental tests are carried out using acoustic waves. The results show that the proposed approach can achieve 3-D location error of 2.77 cm for a target at 0.5 m without synchronization between the transmitter and the receivers. The relatively small system size and sufficient location accuracy provide possibilities in controller tracking for virtual reality applications.

Trilateration is a popular approach in localization. Many related geometric approaches have been proposed for 2D scenarios. In general, each approach has a standard case in which the main solution is applied, and many specific cases. Each specific case has a particular solution, which makes the algorithm more complex. This paper introduces a novel geometric approach that covers all the cases considered by previous algorithms. It turns out though that this approach is a special case of an existing approach, for which we hence provide a geometric interpretation. Numerical results illustrate the method in RSS-based localization while estimating simultaneously the path loss component.

Hand gesture and motion sensing offer an intuitive and natural form of human-machine interface. Air-writing systems allow users to draw alpha-numerical or linguistic characters in the virtual board in air through hand gestures. Traditionally, radar-based air-writing systems have been based on a network of radars, at least three, to localize the hand target through trilateration algorithm followed by tracking to extract the drawn trajectory, which is then followed by recognition of the drawn character by either Long-Short Term Memory (LSTM) utilizing the sensed trajectory or Deep Convolutional Neural Network (DCNN) utilizing a reconstructed 2D image from the trajectory. However, the practical deployments of such systems are limited since the detection of the finger or hand target by all three radars cannot be guaranteed leading to failure of the trilateration algorithm. Further placement of three or more radars for the air-writing solution is neither always physically plausible nor cost-effective. Furthermore, these solutions do not exploit the full potentials of deep neural networks, which are generally capable of learning features implicitly. In this paper, we propose an air-writing system based on a network of sparse radars, i.e. strictly less than three, using 1D DCNN-LSTM-1D transposed DCNN architecture to reconstruct and classify the drawn character utilizing only the range information from each radar. The paper employs real data using one and two 60 GHz milli-meter wave radar sensors to demonstrate the success of the proposed air-writing solution.

International audience; Indoor localization can be based on a matrix of pairwise distances between nodes to localize and reference nodes. This matrix is usually not complete, and its completion is subject to distance estimation errors as well as to the noise resulting from received signal strength indicator measurements. In this paper, we propose to use convolutional neural networks in order to denoise the completed matrix. A trilateration process is then applied on the recovered euclidean distance matrix (EDM) to locate an unknown node. This proposed approach is tested on a simulated environment, using a real propagation model based on measurements, and compared with the classical matrix completion approach, based on the adaptive moment estimation method, combined with trilateration. The simulation results show that our system outperforms the classical schemes in terms of EDM recovery and localization accuracy.

Internet of Things (IoT)-based smart home applications are rising in popularity. However, this trend attracts malicious activity, which causes cost-efficient security to be in high demand. This letter proposes a low-end design that reinforces the security of a home network. It uses private blockchain technology and localization via RSSI-based trilateration. We investigated the benefits of private blockchains over their public counterpart, and we improve the precision of the localization algorithm by testing it against different wireless technologies. The results conclude that using a private blockchain with a WiFi-based communication system produces the most efficient iteration of the proposed design.

Indoor positioning has become a very promising research topic due to the growing demand for accurate node location information for indoor environments. Nonetheless, current positioning algorithms typically present the issue of inaccurate positioning due to communication noise and interferences. In addition, most of the indoor positioning techniques require additional hardware equipment and complex algorithms to achieve high positioning accuracy. This leads to higher energy consumption and communication cost. Therefore, this paper proposes an enhanced indoor positioning technique based on a novel received signal strength indication (RSSI) distance prediction and correction model to improve the positioning accuracy of target nodes in indoor environments, with contributions including a new distance correction formula based on RSSI log-distance model, a correction factor (Beta) with a correction exponent (Sigma) for each distance between unknown node and beacon (anchor nodes) which are driven from the correction formula, and by utilizing the previous factors in the unknown node, enhanced centroid positioning algorithm is applied to calculate the final node positioning coordinates. Moreover, in this study, we used Bluetooth Low Energy (BLE) beacons to meet the principle of low energy consumption. The experimental results of the proposed enhanced centroid positioning algorithm have a significantly lower average localization error (ALE) than the currently existing algorithms. Also, the proposed technique achieves higher positioning stability than conventional methods. The proposed technique was experimentally tested for different received RSSI samples&rsquo, number to verify its feasibility in real-time. The proposed technique&rsquo, s positioning accuracy is promoted by 80.97% and 67.51% at the office room and the corridor, respectively, compared with the conventional RSSI trilateration positioning technique. The proposed technique also improves localization stability by 1.64 and 2.3-fold at the office room and the corridor, respectively, compared to the traditional RSSI localization method. Finally, the proposed correction model is totally possible in real-time when the RSSI sample number is 50 or more.

Lately, we observe an increase in the development of Internet of Things (IoT) systems. The interconnection of multiple devices offers improved solutions in many sectors. In the current paper, we describe the localization framework of a project that incorporates IoT technologies to face unwanted incidents in crowded spaces. The localization framework responds to the scenario of a missing child in crowded outdoor spaces and combines Received Signal Strength Indicator (RSSI), Bluetooth low energy tags and trilateration. The experimental results of trials performed in two outdoor spaces are also reported. This framework is currently being tested in the actual pilot premises.

The accuracy and stability of indoor location method based on received signal strength indication (RSSI) decrease due to multipath effect of wireless signal. With the development of Wi-Fi technology, the IEEE 802.11n series communication protocol and the subsequent wireless LAN protocols use multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM) and other technologies. WI-FI using CSI technology has emerged as a new paradigm of indoor positioning service (IPS). However, the propagation of RSSI and CSI is still affected by indoor multiple paths, and we cannot obtain signals in some corner areas. Therefore, RSSI and CSI need a good calibration method to improve the accuracy of the position estimation system. In this paper, we propose a RSSI and CSI based indoor positioning method. In this method, CSI of physical layer combined with RSSI is used as reference information to reduce multipath attenuation of signal receiver. On this basis, the weighted gray prediction model is used for data processing to reduce the time variability of the received signal. Finally, this method combines with trilateration algorithm to further reduce positioning error. This paper also provides experimental comparisons of our proposed data generation method with existing indoor positioning methods. Experimental results show that the proposed data generation method can significantly improve the positioning accuracy, reduce the computational complexity, and have a better indoor positioning effect than using an effective CSI positioning method. Meanwhile, the proposed method also can obtain more accurate positioning results in corner areas of the indoor environment where WIFI signals cannot be obtained.

To meet the growing demand for indoor wireless positioning, a high-precision indoor positioning system is designed in this paper. The designed system adopts the fusion of multi-source positioning techniques, to achieve the reasonable cost and reliability. In particular, a two-steps fusion algorithm is proposed for the designed system, in which the UWB and ZigBee techniques, the trilateration and the maximum likelihood algorithms are fused in turn through the error comparison, to get the high-precision estimated locations. Then, the proposed positioning system is verified in a plane space of ${\mathrm {10m \times10m}}$ by simulations, and the reference nodes of UWB and ZigBee are designed respectively. The received signal strength (RSS) ranging is used, and the distance measurements of both UWB and ZigBee are established based on it. The results show that the designed system using the multi-source fusion algorithm has better performance than other algorithms and can achieve the demand of high-precision indoor positioning.