Track fusion based on threshold factor classification algorithm in wireless sensor networks

Wang, Xiang; Wang, Tao; Chen, Shiyang; Fan, Renhao; Xu, Yang; Wang, Weike; Li, Hongge; Xia, Tongsheng

doi:10.1002/dac.3164

Cited by 6 publications

(2 citation statements)

References 21 publications

(27 reference statements)

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“…The optimal estimate values are calculated by the predictive values and the observation values based on most of the classical tracking algorithms in the process of range-based closely spaced multi-target localization and tracking [22], which are not accurate and is interfered by the measurement data of closely spaced targets. In the actual case, a large amount of measurement data is received from the sensor network, but it is not clear whether the measurement data belongs to a specific target, and measurement data of all targets is mixed with noise.…”

Section: Introductionmentioning

confidence: 99%

Measurement Data Fusion Based on Optimized Weighted Least-Squares Algorithm for Multi-Target Tracking

Wang

Liu

et al. 2019

IEEE Access

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The outliers remove, the classification of effective measurements, and the weighted optimization method of the corresponding measurement are the main factors that affect the positioning accuracy based on range-based multi-target tracking in wireless sensor networks. In this paper, we develop an improved weighted least-square algorithm based on an enhanced non-naive Bayesian classifier (ENNBC) method. According to the ENNBC method, the outliers in the measurement data are removed effectively, dataset density peaks are found quickly, and remaining effective measurements are accurately classified. The ENNBC method improves the traditional direct classification method and took the dependence among continuous density attributes into account. Four common indexes of classifiers are used to evaluate the performance of the nine methods, i.e., the normal naive Bayesian, flexible naive Bayesian (FNB), the homologous model of FNB (FNB ROT), support vector machine, k-means, fuzzy c-means (FCM), possibilistic c-means, possibilistic FCM, and our proposed ENNBC. The evaluation results show that ENNBC has the best performance based on the four indexes. Meanwhile, the multi-target tracking experimental results show that the proposed algorithm can reduce the root-mean-squared error of the position compared with the extended Kalman filter. In addition, the proposed algorithm has better robustness against large localization and tracking errors. INDEX TERMS Range-based multi-target tracking, wireless sensor networks, weighted least-square, naive Bayesian, localization root mean squared error.

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Section: Introductionmentioning

confidence: 99%

Measurement Data Fusion Based on Optimized Weighted Least-Squares Algorithm for Multi-Target Tracking

Wang

Liu

et al. 2019

IEEE Access

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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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An Efficient Target Tracking in Directional Sensor Networks Using Adapted Unscented Kalman Filter

Izadi-Ghodousi

Hosseinpour

Safaei

et al. 2019

Wireless Pers Commun

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Track fusion based on threshold factor classification algorithm in wireless sensor networks

Cited by 6 publications

References 21 publications

Measurement Data Fusion Based on Optimized Weighted Least-Squares Algorithm for Multi-Target Tracking

Measurement Data Fusion Based on Optimized Weighted Least-Squares Algorithm for Multi-Target Tracking

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

An Efficient Target Tracking in Directional Sensor Networks Using Adapted Unscented Kalman Filter

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