Target localization and tracking based on improved Bayesian enhanced least-squares algorithm in wireless sensor networks

Wang, Tao; Wang, Xiang; Shi, Wei; Zhao, Zongmin; He, Zhenxue; Xia, Tongsheng

doi:10.1016/j.comnet.2019.106968

Cited by 28 publications

(25 citation statements)

References 46 publications

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“…In this section, the proposed clustering method is compared with the DBSCAN, the k-means [19]- [21], the FCM [22], the PCM [5], the AMPCM [20], [23], and the APCM in a typical scene. Then, the experimental results for the scenes 5: using (27) update the cluster centers θ (l) ; 6: using (26) update the center line segment L (l) of each cluster; 7: using (25) update the Kernel function K (l) ; 8: using (31) update the membership degree matrix U (l) ; 9: for i ← 1 to n do Fig.3(b) show that the proposed clustering method is superior than the other several clustering algorithms.…”

Section: Experiments Resultsmentioning

confidence: 99%

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Lane Determination of Vehicles Based on a Novel Clustering Algorithm for Intelligent Traffic Monitoring

Cao

Wang

et al. 2020

IEEE Access

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In intelligent traffic monitoring, speed measuring millimeter waves (MMW) radar is one of the most commonly used tools for traffic enforcement. In traffic enforcement field, the radar must provide the evidence of each vehicle belongs to which lane. In this paper, we propose a novel kernel line segment adaptive possibilistic c-means clustering algorithm (KLSAPCM) for lane determination of vehicles. Firstly, the raw measurement data is preprocessed using the extracting method of data adjacent lane centerlines. Secondly, according to the improved minimum radius data search method, outliers are removed and the proposed KLSAPCM algorithm is initialized. Finally, the accuracy of lane determination has been improved by the proposed KLSAPCM clustering algorithm based on adaptive kernel line segment that conforms to the shape features of the measurement data in the actual scene. The experiment results for multiple scenes were: the KLSAPCM algorithm is compared with the DBSCAN, the k-means, the FCM, the PCM, the AMPCM, and the APCM algorithms on real measurement datasets, and the results highlight the classification rate of the proposed algorithm. Meanwhile, the proposed algorithm gets a good real-time performance and strong robustness for some sparse moving vehicle scene applications. INDEX TERMS MMW radar, radar measurements, lane determination, clustering algorithms. The associate editor coordinating the review of this manuscript and approving it for publication was Chao Tong.

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Section: Experiments Resultsmentioning

confidence: 99%

“…This provides better initialization results for APCM. After the initialization of θ j , we can calculate the corresponding µ ij value based on (22). Then, the initialization of γ j is as follows:…”

Section: B Outliers Remove and Initialization Based On Sample Densitmentioning

confidence: 99%

Lane Determination of Vehicles Based on a Novel Clustering Algorithm for Intelligent Traffic Monitoring

Cao

Wang

et al. 2020

IEEE Access

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“…Absorption bias plays an important role in Equation (3). It is necessary to discuss the relationship between the RMSE and absorption coefficient α f , which is displayed in Figure 7.…”

Section: Scenario With Varying the Absorption Coefficient α Fmentioning

confidence: 99%

“…In recent years, wireless sensor networks (WSNs) have been widely used in many fields [ 1 , 2 , 3 ], such as data collection, environmental monitoring, target localization and tracking, and traffic control. One of the critical applications is the use of ocean sensor networks (OSNs) to locate ocean targets [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Two-Phase Robust Target Localization in Ocean Sensor Networks Using Received Signal Strength Measurements

Zhang

Mei

et al. 2021

Sensors

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Target localization plays a vital role in ocean sensor networks (OSNs), in which accurate position information is not only a critical need of ocean observation but a necessary condition for the implementation of ocean engineering. Compared with other range-based localization technologies in OSNs, the received signal strength (RSS)-based localization technique has attracted widespread attention due to its low cost and synchronization-free nature. However, maintaining relatively good accuracy in an environment as dynamic and complex as the ocean remains challenging. One of the most damaging factors that degrade the localization accuracy is the uncertainty in transmission power. Besides the equipment loss, the uncertain factors in the fickle ocean environment may result in a significant deviation between the standard rated transmission power and the usable transmission power. The difference between the rated and actual transmission power would lead to an extra error when it comes to the localization in OSNs. In this case, a method that can locate the target without needing prior knowledge of the transmission power is proposed. The method relies on a two-phase procedure in which the location information and the transmission power are jointly estimated. First, the original nonconvex localization problem is transformed into an alternating non-negativity-constrained least square framework with the unknown transmission power (UT-ANLS). Under this framework, a two-stage optimization method based on interior point method (IPM) and majorization-minimization tactic (MMT) is proposed to search for the optimal solution. In the first stage, the barrier function method is used to limit the optimization scope to find an approximate solution to the problem. However, it is infeasible to approach the constraint boundary due to its intrinsic error. Then, in the second stage, the original objective is converted into a surrogate function consisting of a convex quadratic and concave term. The solution obtained by IPM is considered the initial guess of MMT to jointly estimate both the location and transmission power in the iteration. In addition, in order to evaluate the performance of IPM-MM, the Cramer Rao lower bound (CRLB) is derived. Numerical simulation results demonstrate that IPM-MM achieves better performance than the others in different scenarios.

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“…Because they cannot adapt to the effects of weather, environment, and light [11,12]. Since radar signals can be well adapted to complex scenes [13], more and more researchers are beginning to use millimeter-wave radar to solve multi-target tracking problems [14,15] in traffic.…”

Section: Introductionmentioning

confidence: 99%

A Novel Density Peak Fuzzy Clustering Algorithm for Moving Vehicles Using Traffic Radar

et al. 2019

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The detection of adjacent vehicles in highway scenes has the problem of inaccurate clustering results. In order to solve this problem, this paper proposes a new clustering algorithm, namely Spindle-based Density Peak Fuzzy Clustering (SDPFC) algorithm. Its main feature is to use the density peak clustering algorithm to perform initial clustering to obtain the number of clusters and the cluster center of each cluster. The final clustering result is obtained by a fuzzy clustering algorithm based on the spindle update. The experimental data are the radar echo signal collected in the real highway scenes. Compared with the DBSCAN, FCM, and K-Means algorithms, the algorithm has higher clustering accuracy in certain scenes. The average clustering accuracy of SDPFC can reach more than 95%. It is also proved that the proposed algorithm has strong robustness in certain highway scenes.

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Target localization and tracking based on improved Bayesian enhanced least-squares algorithm in wireless sensor networks

Cited by 28 publications

References 46 publications

Lane Determination of Vehicles Based on a Novel Clustering Algorithm for Intelligent Traffic Monitoring

Lane Determination of Vehicles Based on a Novel Clustering Algorithm for Intelligent Traffic Monitoring

Two-Phase Robust Target Localization in Ocean Sensor Networks Using Received Signal Strength Measurements

A Novel Density Peak Fuzzy Clustering Algorithm for Moving Vehicles Using Traffic Radar

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