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Finite-time synchronization is concerned for the fractional-order complex-valued fuzzy cellular neural networks (FOCVFCNNs) with leakage delay and time-varying delays. Without using the usual complex-valued system decomposition method, this paper designs the different forms of the controllers by using 2-norm. And we construct the appropriate Lyapunov functional and apply inequality analytical techniques, some new sufficient conditions are obtained to ensure finite-time synchronization of the FOCVFCNNs. The upper bound of setting-time function is obtained. Finally, numerical examples are examined to illustrate the effectiveness of the analytical results.

This paper mainly considers the finite-time synchronization problem of fuzzy inertial cellular neural networks (FICNNs) with time-varying delays. By constructing the suitable Lyapunov functional, and using integral inequality techniques, several sufficient criteria have been proposed to ensure the finite-time synchronization for the addressed (FICNNs). Without applying the known finite-time stability theorem, which is widely used to solve the finite-time synchronization problems for (FICNNs). In this paper, the proposed method is relatively convenient to solve finite-time synchronization problem of the addressed system, this paper extends the research works on the finite-time synchronization of (FICNNs). Finally, numerical simulations illustrated verify the effectiveness of the proposed results.

On 31 July 2020, the Beidou global navigation satellite system (BDS-3) was officially announced as being commissioned. In addition to offering global positioning, navigation, and timing (PNT) services, BDS-3 also provides precise point positioning (PPP) augmentation services. The satellite orbit correction, clock correction and code bias correction of BDS-3 and other global navigation satellite systems (GNSS) are broadcast by the BDS-3 geostationary earth orbit (GEO) satellites through the PPP-B2b signal. The PPP-B2b service is available for users in China and the surrounding area. In this study, an initial assessment of the PPP-B2b service is presented, with collected 3-day PPP-B2b messages. Based on broadcast ephemeris and PPP-B2b messages, the precise satellite orbits and clock offsets can be recovered. This precision is evaluated with the precise ephemeris from the GeoForschungsZentrum Potsdam (GFZ) analysis center as references. The results indicate that the accuracy of BDS-3 satellite orbits in the direction of radial, along-track, and cross-track is 0.138, 0.131, and 0.145 m, respectively, and for GPS a corresponding accuracy of 0.104, 0.160, and 0.134 m, respectively, could be obtained. The precision of clock offsets can reach a level of several centimeters for both GPS and BDS-3. Both the performance of static PPP and kinematic PPP are evaluated using the observations from four international GNSS monitoring assessment service (iGMAS) stations. Regarding static PPP, the average convergence time is 17.7 minutes to achieve a horizontal positioning accuracy of better than 0.3 m, and a vertical positioning accuracy of better than 0.6 m. The average positioning accuracy in the direction of east, north, and up-directions are 2.4, 1.6, and 2.3 cm. As to kinematic PPP, the average RMS values of positioning errors in the direction of east, north, and up are 8.1 cm, 3.6 cm, and 8.0 cm after full convergence.

The high-precision navigation and positioning with GNSS has become widely used in various applications with the development of new GNSS systems, such as BeiDou and Galileo. For marine applications, high-precision navigation and positioning with GNSS is still a challenge since the requirement of the communication link is a problem on sea. Both DGNSS and RTK require data communication between base station on land and the rover station on sea. The data communication can be performed by providers of the marine satellite communication service, such as Intelsat, Eutelsat, Telesat and the SpaceX. However, the cost is too high to be afforded by ordinary GNSS users. The BeiDou short-message service provides an efficient way for the data communication between reference station on land and rover station on sea. Each BeiDou message length is limited to 78 bytes, and the communication frequency is limited to 60 s. Based on the BeiDou short-message service, the high-precision positioning has been achieved in previous studies. However, multiple set of short-message devices are used and the cost is still high. In this research, based on dual-frequency GNSS data, we propose the high-precision navigation on sea with single set of short-message device. The space-relative and time-relative positioning methods are integrated to reduce the data requirements. That is, first, every minute precise position is acquired with traditional space-relative positioning method and then the position of the other epochs is derived with time-relative positioning method. The experimental results based on buoy observations on sea show that the navigation accuracy can reach up to cm level in both horizontal and vertical directions. The proposed method can meet the requirements of different marine applications such as tide monitoring and wave monitoring.

There are two important aspects in optimally processing the acoustic measurements: the establishment of the functional model and the refinement of the associated stochastic model. In most cases the functional model may be sufficiently well-established, but the stochastic model error has not received considerable attention and it still remains a challenging research topic in real-time positioning. The equal weight stochastic model (EWSM) commonly used at present is under the assumption of equal-precision observation, which is imprecise and not coincident with reality. The purpose of this paper is to develop an approach to obtaining more accurate weights of observables by real-time estimating the variance of observations. The inaccurate stochastic model effect is analyzed and the real time stochastic model (RTSM) estimated by using the least squares residual of observations is presented to improve the positioning accuracy. Simulation results have shown that the underwater positioning accuracy goes from 0.173 m to 0.157 m based on GPS Intelligent Buoy (GIB) by employing the real time stochastic model, while that can be improved from 0.426 m to 0.306 m based on surveying vessel (SV) when adopting RTSM. Both simulations based on GIB and SV indicate that real-time stochastic model can perform better than conventional equal weight stochastic model in positioning accuracy.

Band selection is an important data dimensionality reduction tool in hyperspectral images (HSIs). To identify the most informative subset band from the hundreds of highly corrected bands in HSIs, a novel hyperspectral band selection method using a crossover-based gravitational search algorithm (CGSA) is presented in this study. In this method, the discriminative capability of each band subset is evaluated by a combined optimisation criterion, which is constructed based on the overall classification accuracy and the size of the band subset. As the evolution of the criterion, the subset is updated using the V -shaped transfer function-based CGSA. Ultimately, the band subset with the best fitness value is selected. Experiments on two public hyperspectral datasets, i.e. the Indian Pines dataset and the Pavia University dataset, have been conducted to test the performance of the proposed method. Comparing experimental results against the basic GSA and the PSOGSA (hybrid PSO and GSA) revealed that all of the three GSA variants can considerably reduce the band dimensionality of HSIs without damaging their classification accuracy. Moreover, the CGSA shows superiority on both the effectiveness and efficiency compared to the other two GSA variants.

Real-time precise point positioning (RTPPP) is a popular positioning method that uses a real-time service (RTS) product to mitigate various Global Navigation Satellite Systems (GNSS) errors. However, communication links are not available in the ocean. The use of a communication satellite for data transmission is so expensive that normal users could not afford it. The BeiDou short-message service provides an efficient option for data transmission at sea, with an annual fee of approximately 160 USD. To perform RTPPP using BeiDou short messages, the following two challenges should be appropriately addressed: the maximum size of each BeiDou message is 78 bytes, and the communication frequency is limited to once a minute. We simplify the content of RTS data to minimize the required bandwidth. Moreover, the orbit and clock corrections are predicted based on minute-interval RTS orbital and clock corrections. An experiment was conducted to test the performance of the proposed method. The numerical results show that the three-dimensional positioning precision can reach approximately 0.4 m with combined GPS + GLONASS and approximately 0.2 m with combined GPS + GLONASS + Galileo + BeiDou.

An accurate observation model and statistical model are critical in underwater integrated navigation. However, it is often the case that the statistical characteristics of noise are unknown through the ultra-short baseline (USBL) system/Doppler velocity log (DVL) integrated navigation in the deep-sea. Additionally, the velocity of underwater vehicles relative to the bottom of the sea or the currents is commonly provided by the DVL, and an adaptive filtering solution is needed to correctly estimate the velocity with unknown currents. This paper focuses on the estimation of unknown currents and measurement noise covariance for an underwater vehicle based on the USBL, DVL, and a pressure gauge (PG), and proposes a novel unbiased adaptive two-stage information filter (ATSIF) for the underwater vehicle (UV) with an unknown time-varying currents velocity. In the proposed algorithm, the adaptive filter is decomposed into a standard information filter and an unknown currents velocity information filter with interconnections, and the time-varying unknown ocean currents and measurement noise covariance are estimated. The simulation and experimental results illustrate that the proposed algorithm can make full use of high-precision observation information and has better robustness and navigation accuracy to deal with time-varying currents and measurement outliers than existing state-of-the-art algorithms.

Global navigation satellite systems (GNSS) can attain centimeter level positioning accuracy, which is conventionally provided by real-time precise point positioning (PPP) and real-time kinematic (RTK) techniques. Corrections from the data center or the reference stations are required in these techniques to reduce various GNSS errors. The time-relative positioning approach differs from the traditional PPP and RTK in the sense that it does not require external real-time corrections. It computes the differences in positions of a single receiver at different epochs using phase observations. As the code observations are not used in this approach, its performance is not affected by the noise and multipath of code observations. High reliability is another advantage of time-relative precise positioning because the ambiguity resolution is not needed in this approach. Since the data link is not required in the method, this approach has been widely used in remote areas where wireless data link is not available. The main limitation of time-relative positioning is that its accuracy degrades over time between epochs because of the temporal variation of various errors. The application of the approach is usually limited to be within a time interval of less than 20 min. The purpose of this study was to increase the time interval of time-relative positioning and to extend the use of this method to applications with a longer time requirement, especially in remote areas without wireless communication. In this paper, the main error sources of the time-relative method are first analyzed in detail, and then the approach to improve the accumulated time relative positioning method is proposed. The performance of the proposed method is assessed using both static and dynamic observations with a duration as long as several hours. The experiments presented in this paper show that, among the four scenarios tested (i.e., GPS, GPS/Galileo, GPS/Galileo/BeiDou, and GPS/Galileo/BeiDou/GLONASS), GPS/Galileo/BeiDou performed best and GPS/Galileo/BeiDou/GLONASS performed worst. The maximum positioning errors were mostly within 0.5 m in the horizontal direction, even after three hours with GPS/Galileo/BeiDou. It is expected that the method could be used for positioning and navigation for as long as several hours with decimeter level horizontal accuracy in remote areas without wireless communication.

Recently, some smartphone manufacturers have subsequently released dual-frequency GNSS smartphones. With dual-frequency observations, the positioning performance is expected to be significantly improved. Cycle-slip detection and correction play an important role in high-precision GNSS positioning, such as precise point positioning (PPP) and real-time kinematic (RTK) positioning. The TurboEdit method utilizes Melbourne–Wübbena (MW) and phase ionospheric residual (PIR) combinations to detect cycle-slips, and it is widely used in the data processing applications for geodetic GNSS receivers. The smartphone pseudorange observations are proved to be much noisier than those collected with geodetic GNSS receivers. Due to the poor pseudorange observation, the MW combination would be difficult to detect small cycle-slips. In addition, some specific cycle-slip combinations, where the ratio of cycle-slip values at different carrier frequencies is close to the frequency ratio, are also difficult to be detected by PIR combination. As a consequence, the traditional TurboEdit method may fail to detect specific small cycle-slip combinations. In this contribution, we develop a modified TurboEdit cycle-slip detection and correction method for dual-frequency smartphone GNSS observations. At first, MW and PIR combinations are adopted to detect cycle-slips by comparing these two combinations with moving-window average values. Then, the epoch-differenced wide-lane combinations are used to estimate the changes of smartphone position and clock bias, and the cycle-slip is identified by checking the largest normalized residual whether it exceeds a predefined threshold value. The process of estimation and cycle-slip identification is implemented in an iterative way until there is no over-limit residual or there is no redundant measurement. At last, the cycle-slip values at each frequency are estimated with the epoch-differenced wide-lane and ionosphere-free combinations, and the least-square ambiguity decorrelation adjustment (LAMBDA) method is adopted to further obtain an integer solution. The proposed method has been verified with 1 Hz dual-frequency smartphone GNSS data. The results show that the modified TurboEdit method can effectively detect and correct even for specific small cycle-slip combinations, e.g., (4, 3), which is difficult to be detected with the traditional TurboEdit method.

Real-time precise point positioning (PPP) based on open-access real-time service (RTS) of the international GNSS service (IGS) has attracted increasing attention in recent years. For offshore applications, the receiving of RTS corrections becomes a major obstacle due to lack of internet-based infrastructures. Short-message communication (SMC) is a particular technique of BeiDou navigation satellite system (BDS), which can provide bidirectional communication between users in a cost-effective way. However, the limited bandwidth and frequency of BDS SMC make it impossible to directly broadcast IGS RTS corrections with BDS SMC. An offshore real-time PPP technique is proposed based on IGS RTS corrections with a single set of BDS SMC devices for communication. RTS orbit corrections in the along-track, cross-track and radial directions, the clock correction and the code bias correction of each satellite are together converted into an equivalent range correction on the basis of user approximate position and will-be-used code observation types. The range corrections within 1 min are fitted into a first-order polynomial. Only the range and range-rate corrections at the reference time are broadcast with BDS SMC. In addition, different from broadcasting corrections of all satellites in the IGS RTS, corrections of only visible satellites are encoded into a BDS SMC correction message to further reduce the data size. The encoding strategy of BDS SMC correction message and a detailed procedure of the offshore real-time PPP technique are presented. The land-simulated and offshore experiment results show that the proposed technique can meet the demands of high-precision offshore applications.

In the application of GNSS in the velocity determination, it is often the case that some GNSS receivers give an opposite sign for the raw Doppler observations which do not correspond to the real Doppler shift. This is caused by different methods of the GNSS signal processing in different GNSS receivers. If the velocities of kinematic platforms are calculated by using raw Doppler observations from the GNSS receiver directly, the directions of the estimated velocities may be reversed, and the value of the velocity is wrong with respect to the actual movement. This would lead to incorrect results, and unacceptable for research and applications. To overcome this problem, a new method of sign correction for raw Doppler observations is proposed in this study. This algorithm constructs a correction function based on the GNSS carrier-phase-derived Doppler observations. To test this approach, GNSS data of GEOHALO airborne gravimetric missions have been used. The results show that the proposed method, which is straightforward and practical, can produce the correct velocity for a kinematic platform in any case, independent of the internal hardware structure and the specific way of the signal processing of the GNSS receivers in question.

Autonomous underwater vehicle (AUV) acoustic navigation is challenged by unknown system noise and gross errors in the acoustic observations caused by the complex marine environment. Since the classical unscented Kalman filter (UKF) algorithm cannot control the dynamic model biases and resist the influence of gross errors, an adaptive robust UKF based on the Sage-Husa filter and the robust estimation technique is proposed for AUV acoustic navigation. The proposed algorithm compensates the system noise by adopting the Sage-Husa noise estimation technique in an online manner under the condition that the system noise matrices are kept as positive or semi positive. In order to control the influence of gross errors in the acoustic observations, the equivalent gain matrix is constructed to improve the robustness of the adaptive UKF for AUV acoustic navigation based on Huber&rsquo, s equivalent weight function. The effectiveness of the algorithm is verified by the simulated long baseline positioning experiment of the AUV, as well as the real marine experimental data of the ultrashort baseline positioning of an underwater towed body. The results demonstrate that the adaptive UKF can estimate the system noise through the time-varying noise estimator and avoid the problem of negative definite of the system noise variance matrix. The proposed adaptive robust UKF based on the Sage-Husa filter can further reduce the influence of gross errors while adjusting the system noise, and significantly improve the accuracy and stability of AUV acoustic navigation.

Cognitively inspired swarm intelligence algorithms (SIAs) have attracted much attention in the research area of clustering since it can give machine the ability of self-learning to achieve better classification results. Recently, the SIA-based multi-objective optimization (MOO) methods have shown their superiorities in data clustering. However, their performances are limited when applying to the clustering of remote sensing imagery (RSI). To construct an excellent MOO-based clustering method, this paper presents a social recognition-based multi-objective gravitational search algorithm (SMGSA) to achieve simultaneous optimization of two conflicting cluster validity indices, i.e., the Xie-Beni (XB) index and the Jm index. In the SMGSA, searching particles not only are guided by those elite particles stored in an external archive by the gravitational force but also learn from the social recognition of the whole population through the position difference. SMGSA thereby formed with outstanding exploitation ability. Comparison experiments on two public RSI data sets, including a moderate aerial image and a hyperspectral, validated that the MOO-based clustering methods could obtain more accurate results than the single validity index-based method. Moreover, the SMGSA-based method can achieve superior results than that of the multi-objective gravitational search algorithm without social recognition ability. The proposed SMGSA performs favorable balance between the two conflicting cluster validity indices and achieves preferable classification for the RSI. In addition, this study indicates that the swarm intelligence-based cognitive computing is potential for the intelligent interpretation and understanding of complicated remote sensing scene.

Traditionally, if full ambiguity resolution is not successful, partial ambiguity resolution (PAR) will be tried. However, identifying which subset of ambiguities to fix is not easy and is still an ...

This paper investigates the joint optimization problem on the logistics infrastructure investment and CO2 emission taxes for a sustainable city logistics network design by a goal programming approach where the cost recovery, service level and CO2 emission reduction goals are involved. The above multi-objective logistics infrastructure capacity investment and CO2 emission taxes problem is formulated as a bi-level goal programming model. Given the priority structure of the goals, the total deviations from predetermined goals are minimized in the upper level, while the lower level of the model serves as the service route choice equilibrium problem of logistics users. To solve the proposed model, a genetic algorithm is developed, where the method of successive average (MSA) is embedded. The case study focusing on the urban logistics network of Changsha, China demonstrates the effectiveness of the bi-level goal programming model and the genetic algorithm. The findings reveal that the priority rankings of the goals have a significant impact on the joint decisions of CO2 emission taxes and logistics infrastructure capacity investment. The proposed methodology provides an avenue to balance multiple conflicting objectives and obtain an economical and environmental city logistics network.

This paper proposes a multi-scale segmentation approach for high resolution remote sensing image (HRRSI) based on the gravitational field and region merging. In this approach, the HRRSI is firstly transformed into a gravitational field by incorporating the spatial and spectral information. Based on which, the attraction among neighboring pixels will cause travelling of each pixel. During the travelling, pixels with similar spectral information which are at the nearby location get grouped, which define an initial segmentation for the HRRSI which is often over-segmented in places. Then, an improved graph based region merging method is adopted to merge the over-segmented regions which yield the multi-scale segmentation results. To evaluate the proposed approach, we conduct extensive experiments on three HRRSIs from different sensors and the obtained results are compared with those of eCognition's multi-resolution segmentation method. The experimental results show that the proposed approach reduces much more over-segmentation problem and produces more accurate image segmentation.

The high interior heterogeneity of land surface covers in high-resolution image of coastal cities makes classification challenging. To meet this challenge, a Multi-Scale Superpixels-based Classification method using Optimized Spectral−Spatial features, denoted as OSS-MSSC, is proposed in this paper. In the proposed method, the multi-scale superpixels are firstly generated to capture the local spatial structures of the ground objects with various sizes. Then, the normalized difference vegetation index and extend multi-attribute profiles are introduced to extract the spectral−spatial features from the multi-spectral bands of the image. To reduce the redundancy of the spectral−spatial features, the crossover-based search algorithm is utilized for feature optimization. The pre-classification results at each single scale are, therefore, obtained based on the optimized spectral−spatial features and random forest classifier. Finally, the ultimate classification is generated via the majority voting of those pre-classification results in each scale. Experimental results on the Gaofen-2 image of Qingdao and WorldView-2 image of Hong Kong, China confirmed the effectiveness of the proposed method. The experiments verify that the OSS-MSSC method not only works effectively on the homogeneous regions, but also is able to preserve the small local spatial structures in the high-resolution remote sensing images of coastal cities.

To meet the demands of civil aviation and other precise navigation applications, several satellite-based augmentation systems (SBASs) have been developed around the world, such as the Wide Area Augmentation System (WAAS) for North America, the European Geostationary Navigation Overlay Service (EGNOS) for Europe, the Multi-functional Satellite Augmentation System (MSAS) for Japan, the GPS (Global Positioning System) Aided GEO Augmented Navigation (GAGAN) for India, and the System for Differential Corrections and Monitoring (SDCM) for Russia. The SBASs broadcast messages to correct satellite orbit, clock, and ionosphere errors to augment the GPS positioning performance. In this paper, SBAS orbit, clock and ionospheric corrections are evaluated. Specifically, the orbit, clock and ionospheric corrections derived from SBAS messages are comprehensively evaluated using data collected from the above mentioned systems over 181 consective days. The evaluation indicates that the EGNOS outperforms other systems with signal-in-space range error (SISRE) at 0.645 m and ionospheric correction accuracy at 0.491 m, respectively. Meanwhile, the accuracy of SDCM is comparable to EGNOS with SISRE of 0.650 m and ionospheric correction accuracy of 0.523 m. For WAAS, the SISRE is 0.954 m and the accuracy of ionospheric correction is 0.505 m. The accuracies of the SBAS corrections from the MSAS and GAGAN systems, however, are significantly worse than those of others. The SISREs are 1.931 and 1.325 m and the accuracies of ionospheric corrections are 0.795 and 0.858 m, for MSAS and GAGAN, respectively. At the same time, GPS broadcast orbit, clock and ionospheric corrections are also evaluated. The results show that there are no significant improvements in the SISRE of the broadcast navigation data by applying SBAS corrections. On the other hand, the accuracy of SBAS ionospheric corrections is still much better than GPS broadcast ionospheric corrections, which could still be beneficial for single-frequency users.

Real-time single-frequency precise point positioning (RT-SF-PPP) has become a desired positioning approach because it can achieve high positioning accuracy with a low-cost global navigation satellite system (GNSS) chipset or receiver. For single-frequency precise point positioning (SF-PPP) applications, the ionospheric delay is a dominant error source, and thus the quality of applied ionospheric products is critical to the performance of SF-PPP. To meet the demands of the RT-SF-PPP users, the international GNSS service (IGS) is planning to provide open-access real-time ionospheric products. By now, the Centre National d’Etudes Spatiales (CNES) is the only IGS analysis center (AC) to broadcast real-time ionospheric vertical total electron content (VTEC) message through its real-time service (RTS). The quality of the CNES real-time ionospheric products is drawing increasing attention from the GNSS community. We evaluate the quality of CNES real-time VTEC message both in the ionospheric correction domain and positioning domain. First, 374 consecutive days of CNES VTEC products are collected and compared with the IGS final global ionospheric map (GIM) products. Second, slant total electron content (STEC) computed with CNES VTEC message is fully assessed with respect to STEC derived from dual-frequency GNSS measurements. Finally, RT-SF-PPP is conducted for assessing the quality of CNES real-time ionospheric products in the positioning domain. The degree and order of the spherical harmonic expansions broadcasted in the CNES VTEC messages changed from 6 to 12 in the time span of collected data, the effects of higher degree and order parameters are investigated at the same time in the experiments above.

Global navigation satellite system/acoustic (GNSS/A) underwater positioning technique has drawn increasing attention in the fields of marine scientific research and engineering application. The conventional positioning strategy of GNSS/A is to fix the positions of sea-surface transducer, in other words, the positions of the transducer determined by GNSS positioning at different epochs are regarded as accurate and unbiased, and then estimate the underwater transponder's coordinate with acoustic ranging data. However, there inevitably exists bias in the estimation of transducer's positions, and the precision varies at different epochs. Ignoring precision differences of the transducer's positions will lead to a worse estimation of the transponder's coordinate. In this contribution, we propose a total adjustment (TA) method, where the positions of both sea-surface transducer and seafloor transponder are treated as unknown parameters, and the transducer's positions determined by GNSS positioning at different epochs are introduced as virtual observations. In order to reduce the computation load caused by large increase of estimation parameters, an equivalent transformation is applied to observation equations to remove estimation parameters for the transducer's positions at different epochs. To evaluate the performance of the proposed total adjustment method, a series of simulation tests and a sea-trail experiment were carried out. Testing results show that, as compared with the traditional method, the positioning accuracy with the proposed method can be improved by about 10%–33% in the simulation tests and 15% in the field test.

Multi-objective optimization (MOO) is an important research topic in both science and engineering. This paper proposes a diversity-enhanced and memory-based multi-objective gravitational search algorithm (DMMOGSA). We combine the memory of the best states of individual particles and their population in their evolution paths and the gravitational rules to construct a new search strategy. Under this strategy, the position and mass states of each particle are updated based on the memory associated with it and the current states of all particles in the current population in terms of their gravitational forces on it. A novel diversity-enhancement mechanism is also employed to control the velocity of each particle for traveling to a new position. Experiments were conducted on 12 well-known benchmark functions, and for each function the results of DMMOGSA were compared with those of SPEA2, NSGA-II and MOPSO. Our results show that DMMOGSA can reduce the effect of premature convergence and achieve more reliable performance on most of the tested cases.

Gravitational search algorithm (GSA) has been successfully applied to many scientific and engineering applications in the past few years. In the original GSA and most of its variants, every agent learns from all the agents stored in the same elite group, namely Kbest. This type of learning strategy is in nature a fully-informed learning strategy, in which every agent has exactly the same global neighborhood topology structure. Obviously, the learning strategy overlooks the impact of environmental heterogeneity on individual behavior, which easily resulting in premature convergence and high runtime consuming. To tackle these problems, we take individual heterogeneity into account and propose a locally informed GSA (LIGSA) in this paper. To be specific, in LIGSA, each agent learns from its unique neighborhood formed by k local neighbors and the historically global best agent rather than from just the single Kbest elite group. Learning from the k local neighbors promotes LIGSA fully and quickly explores the search space as well as effectively prevents premature convergence while the guidance of global best agent can accelerate the convergence speed of LIGSA. The proposed LIGSA has been extensively evaluated on 30 CEC2014 benchmark functions with different dimensions. Experimental results reveal that LIGSA remarkably outperforms the compared algorithms in solution quality and convergence speed in general.

Cycle slip detection and correction is very important for high-precision positioning with GNSS. In past research, a lot of work has been done on this subject, especially for current dual-frequency GPS (the global positioning system). However, few have been done about the reliability. Similarly to ambiguity resolution, reliability is important to cycle slip detection and correction. In this research work, methods on how to assess and ensure the reliability of cycle slip detection and correction are proposed.

Improving the accuracy of edge pixel classification is an important aspect of using convolutional neural networks (CNNs) to extract winter wheat spatial distribution information from remote sensing imagery. In this study, we established a method using prior knowledge obtained from statistical analysis to refine CNN classification results, named post-processing CNN (PP-CNN). First, we used an improved RefineNet model to roughly segment remote sensing imagery in order to obtain the initial winter wheat area and the category probability vector for each pixel. Second, we used manual labels as references and performed statistical analysis on the class probability vectors to determine the filtering conditions and select the pixels that required optimization. Third, based on the prior knowledge that winter wheat pixels were internally similar in color, texture, and other aspects, but different from other neighboring land-use types, the filtered pixels were post-processed to improve the classification accuracy. We used 63 Gaofen-2 images obtained from 2017 to 2019 of a representative Chinese winter wheat region (Feicheng, Shandong Province) to create the dataset and employed RefineNet and SegNet as standard CNN and conditional random field (CRF) as post-process methods, respectively, to conduct comparison experiments. PP-CNN’s accuracy (94.4%), precision (93.9%), and recall (94.4%) were clearly superior, demonstrating its advantages for the improved refinement of edge areas during image classification.

Underwater Towed Vehicle navigation and localization in deep sea environments are particularly challenging without high-precision inertial sensors. In this paper, the multi-sensors navigation fusion method of a towed underwater vehicle based on the ultrashort baseline (USBL) system, Doppler velocity log (DVL) and a pressure gauge in the deep sea is investigated. Combined with GPS real-time tide detection technology, the positioning principle of the USBL based on the depth constraint is given, and a straight-line model be proposed to estimate the position of the vehicle used the average heading when underwater towed vehicle is working normally in deep sea without DVL. The interacting multiple model (IMM) algorithm based on smoothing algorithm is proposed to improve the positioning accuracy in the deep sea, and the nonlinearity of the model, precision and stability of filtering are considered simultaneously using the adaptive robust square root cubature Kalman filter. The results of the simulated and practical experiments at 4800 m verify that the new algorithm can take full use of high-precision observation information and greatly improve the positioning accuracy and robustness of a deep sea underwater towed system.

In recent years, the API level 24 allow the user have access to the GNSS raw measurements including pseudorange, doppler and carrier phase, which opens a new era for precise navigation on smart devices. As we known, many manufactures produce smartphones that access raw GNSS measurements, like Google, Huawei, Samsung et al. Different manufactures may use different GNSS chipset, which may result in different performance of measurements and navigation. The objective of this paper is to assess the performance of different types of smartphones including Huawei Mate9, Huawei P10, Samsung Note8, Google Pixel2 and HTC U11+ in open sky area. All of these smartphones are declared to be able to track GPS, BDS, Galileo and GlONASS satellites and access the pseudorange, and the first-three can also output the carrier phase measurements . Firstly, the tracked satellites number, SNR and their NMEA results were compared with that from nearby Trimble-R10. The result shows that each types of smartphone has its advantages and disadvantages. The Huawei Mate9 and Huawei P10 tracked fewer BDS and Galileo satellites, but they can track many satellites from GPS and GLONASS. For Google Pixel2 and HTC U11+, BDS satellites can be tracked only sometimes and also some Galileo satellites for Google Pixel2. Samsung Note8 can receive satellite data from all operational GNSS systems stably, though the satellites number is fewer for each system. For Samsung Note8, Google Pixel2 and HTC U11+, the SNR of most trackable satellites is above 20 and just few of them are between 10 and 20, while for Huawei Mate9 and Huawei P10, many satellite can be tracked whose SNR is between 10 and 20. Statistics results of the NMEA show that the accuracy of NMEA from Huawei Mate9 and Huawei P10 is much better than that from the other three phones. The accuracy of NMEA form Huawei P10 is highest and its RMS are 1.77m and 0.99m in north and east respectively. Secondly, The analysis of double-differenced pseudorange residual indicates that the UTC 11+ performs worst and its doubledifferenced residual varies within ±50m, the other four phones are almost at the same level and vary within ±30m. Double-differenced carrier phase residual shows obvious duty-cycle for all these smartphones, which limit the use of carrier phase. As different smartphone has different implementations of duty-cycle, the clock behaviors were analyzed and compared with each other, which allow us characterize the duty-cycle of these devices. We concluded that the higher precision compensated for temperature variations (TCXO)Hardware clock is not continuous (turning on and off) during the non-tracking periods for Google Pixel2 and HTC U11+, while for Huawei Mate9, Huawei P10 and Samsung Note8, the TCXO is turned off and low-power consumed crystal oscillator (XO) is providing time. After a comparison of the positioning result with raw pseudorange measurements, we did not find obvious difference for the five devices, then the doppler measurements are introduced to smooth the pseudorange and it can improve the accuracy effectively.

The international GNSS service (IGS) has been providing an open-access real-time service (RTS) since 2013, which allows users to carry out real-time precise point positioning (RT-PPP). As the availability of RTS products is vital for RT-PPP, a disruption in receiving RTS products will be a concern. Currently, the IGS Ultra-rapid (IGU) orbit is accurate enough to be used as an alternative orbit for RTS during RTS outages, while the precision of the IGU predicted clock offsets is far below that of the RTS clock product. The existing clock prediction methods based on received RTS clock data will not work well if the discontinuity arises shortly after the start of the RT-PPP processing due to the lack of RTS clock data to fit the prediction model or to predict clock offsets at a high precision. Even if there is a sufficient amount of RTS clock data available, saving large amounts of RTS clock data would also use processor memory. An alternate approach for GPS clock prediction is proposed. The prediction model, composed of linear polynomial and sinusoidal terms, is similar to those used by the precious methods. The main innovation is the determination of the model coefficients: coefficients of linear and sinusoidal terms are estimated with the epoch-differenced clock offsets from the IGU observed part, while the constant coefficient is computed with the latest RTS clock corrections. There is no need to save the received RTS clock corrections, and clock prediction can be carried out even with only one epoch of RTS data. Evaluation of the proposed method shows that the predicted clock offsets within a short period of prediction time, e.g., 5 min, are slightly worse than RTS clock data. Even when the predicted time reaches up to 1 h, the precision of the predicted clock offsets is still higher than that of IGU predicted clock offsets by about 50%.

The focused plenoptic cameras capture the scenes by one snap, which means calibration is not required. Therefore it is a great advantage to obtain the depth information. However, the existing algorithms to obtain the depth information are complicated and time consuming. In this paper, we put forward a depth measurement method based on the data obtained by focused plenoptic camera. The simulation experiment proved that this method is time-saving and has a good performance compared with the classical method.

Global Navigation Satellite System-Acoustic (GNSS-A) combined positioning is the main technique for seafloor geodetic control points localization, typically including datum transfer and subsequent positioning adjustment. This conventional step-wise method is time-consuming and accuracy-limited. In this paper, a novel one-step integrated positioning method for seafloor control network localization is proposed, which estimates positions of surveying vessels and seafloor points simultaneously. The effectiveness of new method is verified by computer simulation and comparative analysis. First, the conventional underwater acoustic positioning stepwise adjustment method is introduced and the positioning errors are analyzed. Then, to better constrain positions, the baseline lengths and depth-differences between pair of seafloor transponders are taken into consideration. The novel integrated positioning model based on overall adjustment is established. Furthermore, to deal with rank deficiency problem in overall adjustment, selecting weight fitting (SWF) method is applied in consideration of reliable priori information of surveying vessel coordinates. Finally, the regulating parameter determination for SWF method is discussed. Tests demonstrate that depth-differences constraint performs better than baseline length constraint in light of vertical geometry weakness. The novel method is notably superior to the conventional method in terms of positioning accuracy according to rms (Root Mean Square) statistics.

This research is aiming for cycle slip detection and correction in case of ionospheric scintillation. Different from the normally discussed situation without ionospheric scintillation, ionospheric delay cannot be neglected due to abrupt ionosphere change. In this case, ionosphere-free testing quantities for cycle slip detection and correction have to be used. In the past research, several geometry-free and ionosphere-free testing quantities have been proposed and in every of them, code measurements are involved. So these geometry-free and ionosphere-free testing quantities are sensitive to code observation noise and multipath. In this research, non-geometry-free and ionosphere-free testing quantities are proposed. Different from previously proposed, the code measurements are not longer involved. Detailed discussion on cycle slip detection and correction in case of ionospheric scintillation are given and method using the new proposed non-geometry-free testing quantities in combination with one previously proposed geometry-free & ionosphere-free wide-lane testing quantity is described. The cycle slip detection and correction performance may not be very reliable with the new proposed method. But it is expected that this research will benefit for future work in this aspect.

Simultaneous optimization of different validity measures can capture different data characteristics of remote sensing imagery (RSI) and thereby achieving high quality classification results. In this paper, two conflicting cluster validity indices, the Xie-Beni ( XB ) index and the fuzzy C-means (FCM) ( Jm ) measure, are integrated with a diversity-enhanced and memory-based multi-objective gravitational search algorithm (DMMOGSA) to present a novel multi-objective optimization based RSI classification method. In this method, the Gabor filter method is firstly implemented to extract texture features of RSI. Then, the texture features are syncretized with the spectral features to construct the spatial-spectral feature space/set of the RSI. Afterwards, cluster of the spectral-spatial feature set is carried out on the basis of the proposed method. To be specific, cluster centers are randomly generated initially. After that, the cluster centers are updated and optimized adaptively by employing the DMMOGSA. Accordingly, a set of non-dominated cluster centers are obtained. Therefore, numbers of image classification results of RSI are produced and users can pick up the most promising one according to their problem requirements. To quantitatively and qualitatively validate the effectiveness of the proposed method, the proposed classification method was applied to classifier two aerial high-resolution remote sensing imageries. The obtained classification results are compared with that produced by two single cluster validity index based and two state-of-the-art multi-objective optimization algorithms based classification results. Comparison results show that the proposed method can achieve more accurate RSI classification.

We present a high resolution 3D visual information acquiring method by fusing 3D information of structured light vision measurement system with the high resolution grayscale image captured by the 2D line-scan camera.

Multilevel image thresholding plays a crucial role in analyzing and interpreting the digital images. Previous studies revealed that classical exhaustive search techniques are time consuming as the number of thresholds increased. To solve the problem, many nature-inspired algorithms (NAs) which can produce high-quality solutions in reasonable time have been utilized for multilevel thresholding. This chapter discusses three typical kinds of NAs and their hybridizations in solving multilevel image thresholding. Accordingly, a novel hybrid algorithm of gravitational search algorithm (GSA) with genetic algorithm (GA), named GSA-GA, is proposed to explore optimal threshold values efficiently. The chosen objective functions in this chapter are Kapur’s entropy and Otsu criteria. This chapter conducted experiments on two well-known test images and two real satellite images using various numbers of thresholds to evaluate the performance of different NAs.

The Chinese BeiDou Navigation Satellite System (BDS) has been an important constitute of the Global Navigation Satellite System (GNSS), and the combination of GPS and BDS shows significant improvements when compared with single GPS system for real-time kinematic (RTK) positioning, and improves on availability and fixing rates, especially in the East Asian area. While network RTK might have different types of receivers, both for global and regional networks, different types of receiver may adopt different internal multipath mitigation methods and other techniques that result in different pseudorange characteristics, especially for a multipath. Then, the performance of wide-lane ambiguity resolution (WL AR) is affected. In this study, we first analyze and compare the characteristics of BDS dual-frequency observations for different types of receivers, including Trimble, Leica, Javad, and Septentrio, based on multipath (MP) observables, and then we assess their influence on double-differenced (DD) WL AR. The numerical results show that an obvious low-frequency component exists in MP observables of BDS geostationary earth-orbit satellites (GEOs) for Leica receivers, while its high-frequency measurement noise is very small. For geosynchronous orbit satellites (IGSOs) and medium earth-orbit satellites (MEOs), a slight fluctuation can also be observed that is similar to that of GPS satellites, except for the satellite-included code bias. In Trimble, Javad, and Septentrio receivers, the MP series are dominated by high-frequency measurement noise, both for GEOs and non-GEOs, except for satellite-included code bias. Furthermore, the characteristic of Leica receivers for BDS GEOs seriously affects WL AR and, even for a short baseline, it takes a long time for WL ambiguities to converge, or not converge for many GEO-related DD WL ambiguities, while Trimble, Javad, and Septentrio receivers perform well for short and medium baselines. Then, a time-difference method is proposed to mitigate the multipath of BDS GEOs for a Leica receiver. After applying the proposed method, WL ambiguity fixing rates of GEO-related satellite pairs are improved significantly and the convergence time is shortened from several hours to ten minutes.

Gait recognition is an important problem in video surveillance. Most of the existing approaches have provided good performance, but they are often affected by views. In this regard, we present the piecewise approach method for view estimation before recognizing gait. Given a gait sequence, the silhouettes in a gait cycle are obtained, and the stable head and shoulder models are extracted. The Procrustes Shape Analysis (PSA) is applied to analysis the head and shoulder models to get the mean shape. The mean shape is converted to distance series. At last, the distance series is converted to piecewise approximation (PA) series. The PA series represents a view. The method is evaluated on CASIA B database. The results show that the method can recognize view correctly.

Most FPGA support floating-point IP core nowadays, but we have paid little attention to the acquisition of data source. A conversion method is advanced in this paper for the reason. A certain range of real number presented by ASCII code is converted to single precision floating-point by pipeline processing with VHDL language. Through functional simulation and download verification, the conversion time is about 10−1us when the clock is 50 MHz. The conversion accuracy of this method can reach 10−9 calculated by MATLAB software. The design in this paper has a rational construction and can provide data source for floating-point IP core.

Precise Point Positioning (PPP) is a method that performs precise position determination in static or kinematic mode using a single GPS receiver. It has been recognized as a very valuable tool in many applications such as in geodetic and geodynamic, in geophysics and meteorology. This paper focuses on a sequential least squares estimation for unknown parameters and accuracy analysis of GPS kinematic Precise Point Positioning (PPP) with marine data. A least squares solution with a priori weighted constraints to the parameters is mainly introduced. The adjustment procedure which is an effectively sequential filter, considers the variations in the states of the parameters between observation epochs and uses appropriate stochastic process noise to update their variances. Combining IGS precise orbit and clock products with ship-borne GPS dual-frequency code and carrier phase observations collected in the sailing and floating state, the validity of the sequential least squares solution and accuracy of GPS kinematic PPP is tested and verified. The results show that the precision of centimeter can be obtained, which can meet the requirements of oceanographic kinematic users.

This paper mainly discusses the application of D- InSAR technique for monitoring the coal mining subsidence in Jibei mine area. Firstly, it introduces the principle and data processing of D-InSAR technique. Then it uses ENVISAT ASAR data and SRTM DEM to carry out 2-pass D-InSAR test. It gets the deformation map and interprets the result. At last, it analyzes some problems to study further in monitoring the mining subsidence using D-InSAR technique. The test proves that D- InSAR technique is a powerful and effective tool to measure the mining land subsidence.

The visualization in a single view of abnormal association patterns obtained from mining lengthy marine raster datasets presents a great challenge for traditional visualization techniques. On the basis of the representation model of marine abnormal association patterns, an interactive visualization framework is designed with three complementary components: three-dimensional pie charts, two-dimensional variation maps, and triple-layer mosaics; the details of their implementation steps are given. The combination of the three components allows users to request visualization of the association patterns from global to detailed scales. The three-dimensional pie chart component visualizes the locations where more marine environmental parameters are interrelated and shows the parameters that are involved. The two-dimensional variation map component gives the spatial distribution of interactions between each marine environmental parameter and other parameters. The triple-layer mosaics component displays the detailed association patterns at locations specified by the users. Finally, the effectiveness and the efficiency of the proposed visualization framework are demonstrated using a prototype system with three visualization interfaces based on ArcEngine 10.0, and the abnormal association patterns among marine environmental parameters in the Pacific Ocean are visualized.