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Exploiting a single-antenna receiver to realise three-dimensional (3D) positioning in a millimetre-wave (mmWave) system is considered. The primary motivation is that the massive antenna arrays will be deployed in the fifth-generation (5G) base stations shortly soon, which not only tremendously promote the data transmission rate, but also enable the user equipment to realise high-precision positioning with a single antenna. Based on the sparsity of the mmWave channel, the tensor decomposition is proposed to be utilised as an effective mathematical tool to realise 3D positioning. Specifically, the authors model the received signals as a third-order tensor for the inherent third-order low-rank tensor structure of the mmWave channel with a single-antenna receiver and then, the positioning parameters (including the angles of departure and the time of arrival) are estimated from the corresponding factor matrices via CAMDECOMP/PARAFAC (CP) decomposition. Moreover, Cramer–Rao bounds (CRBs) on 3D position uncertainty are derived. Numerical results demonstrate that the proposed method based on CP decomposition realises nearly the same positioning accuracy as the state-of-the-art compressed sensing-based algorithm in the 5G mmWave systems with lower computation complexity, and the root mean square errors of the 3D positioning results obtained via the proposed approach are close to their CRBs.

The indoor positioning algorithm based on visual object recognition is a new kind of indoor positioning solution, in which, user’s position can be determined by steps as identifying objects around, matching general positions by a digital map and precise positioning through nonlinear calculation. However, limited by low objects detection rate and low detection accuracy, the availability of the localization algorithm is greatly affected. Therefore, this paper presents a novel method that utilizes Fully Convolutional Networks (FCN), which brings a great improvement in contour recognition accuracy when identifying objects in an image, and thus improving the final positioning results. In additional, a pre-trained CNN (Convolution Neural Network) is also proposed in the paper, which is used to get a coarse location for solving the problem of fuzzy floor and location brought by similar object distribution in the existence algorithm. Verifying by experimental tests, The results show that compared with the previous TM-based (template matching) and the R-CNN based localization algorithm (Region-based Convolution Neural Network), the methods proposed in this paper can effectively improve the success rate from 85% to 93% and the positioning error drop from 2.6m to 1.4m of the indoor positioning algorithm.

As modern architectural structures have become more and more complicated, indoor navigation is getting increasingly important. The indoor objects-based positioning algorithm is a novel indoor positioning solution, which locates the users by taking images indoor, recognizing objects, extracting related information and matching with digital maps. However, Tedious surveying work in the process of pre-layout, high computational complexity and a trend of divergence on linear solutions still restricts the performance of the method. Thus, this paper proposes an improved indoor navigation method based on monocular vision measuring and Region-based Convolutional Network(RCNN), improving the efficiency by converting the two-dimensional spatial structure presented in the photo into three-dimensional space and thereby improving the success rate of indoor positioning In order to verify the performance of the proposed algorithm, a test is conducted inside a typical structure. The result shows that comparing with the former algorithm, the proposed method provides 9.5% higher success rate at 61.9% with 92.9% fail safe rate.

This study presents a novel method that utilizes Region-based Convolution Neural Network (R-CNN) for identifying objects in an image, and then, localizes the image using a pre-stored map of these objects. The algorithm follows the next steps. First, an image, taken by a smart phone, is uploaded to the server, where a pre-trained R-CNN and template images are combined to identify the objects in the image. In the second step, the location of the image is derived according to the spatial relationships of the objects in the image with a prior knowledge of the objects location in a digital map. Finally, if more than one location solutions are obtained from the previous step, a range-based localization method is utilized to eliminate the wrong results. To prove the concept, a test is conducted in typical yet challenging indoor environment. Selected objects were labeled in 600 images, taken randomly around the test environment, to train the R-CNN and used for template matching. Another 42 images, with known capture locations, were utilized to assess the performance of the method. The proposed object-based indoor localization method detected 81.7% of the objects in the images, and provided 59.5% success rate with 1-5 m accuracy.

When flying through undulating terrain or flying with a large tilt angle, the existing LiDAR navigation algorithms have difficulty in outputting continuous, stable and high-precision navigation information, which seriously affects the accuracy and robustness of LiDAR/INS integrated navigation system. To solve the above problems, this paper proposes an adaptive switching algorithm based on Sigmoid function. By judging the platform inclination and fluctuation degree of the scanning area, an adaptive switching mechanism of two LiDAR navigation methods of ICP and SLPF is constructed. The simulation results show that the airborne LiDAR/INS integrated navigation method proposed in this paper can greatly improve the position estimation accuracy when the platform is flying through the undulating terrain or flying with a large tilt angle (pitch angle, roll angle). Compared with the existing CMLP, ICP and SLPF algorithms, the proposed LiDAR/INS integrated navigation algorithm has improved the three-dimensional positioning accuracy by 78.7%, 53.7% and 18.3% respectively.

Long Term Evolution (LTE) is a promising positioning system for its appropriate positioning characteristics and excellent coverage, especially in the harsh environments such as urban canyons and indoor scenarios. However, most of the researches so far on LTE positioning algorithms are based on time parameters. Thus in this paper, an algorithm based on signal parameters estimation via rotational invariance techniques (ESPRIT) to jointly estimate the time-of- arrival (TOA) and the direction-of-arrival (DOA) from received LTE signals in multipath environment is presented. This method estimates the TOA parameters first after modeling the received LTE signals in the frequency domain. Then the DOA parameters via the difference of the TOA are obtained, and thereby the joint TOA and DOA estimation is gained. Simulation results show that this method performs well on different signal-to-noise-ratios, numbers of multipath and transmission bandwidths. Moreover, the simulation results show that the proposed algorithm has much better performance than multiple signal classification (MUSIC) algorithm and propagator method (PM) and its positioning accuracy can achieve centimeter level.

A Single Station and Single Antenna Direction Measurement Method has been developed to get the direction between the target and carrier. This method can get the position, velocity, attitude (PVA) and the Doppler between target and carrier through the coupled navigation federal data fusion filter. The direction can be calculated through the attitude information and velocity information. The path planning algorithm can eliminate the false target. The simulation results have shown that this method can get the direction between target and carrier with single station and single antenna. And it can also improve the measurement accuracy and the performance of the system.