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

Abstract

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.

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

“…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.…”

“…At the intersection of roads or highways, the radar speedometer is installed to monitor the speed of passing vehicles. However, due to technical limitations, some false detections and missed detections may occur during the speed measurement process, and the intelligent lane separation algorithms can greatly improve this effect [3]- [5]. However, in the application of actual scenes, it is often found that these lane separation algorithms are not accurate in judging the lane of illegal vehicles, which triggers the camera of the wrong lane, resulting in the illegal vehicle capture failure and escape from legal sanctions.…”

A LS-SVM based Measurement Points Classification Algorithm for Adjacent Targets in WSNs

“…In order to evaluate the performance of the proposed algorithm for tracking adjacent targets, several simulations have been done and compared with some existing algorithms, namely K-means [16], Fuzzy C-means(FCM) [14] and EKFCM [13] clustering algorithm, which are all combined with the same tracking EKF algorithm. Besides, the root mean square error (RMSE) and Mean RMSE (MMSE) of the position are chosen as performance metrics [25].…”

“…In general, there are two kinds of state-of-the-art methods dealing with sampling points for respective targets: with or without the probability model [11][12][13][14][15]. While the computational complexity of the former increases as the model becomes more complicated, the latter saves a large number of complex calculations and includes two different methods: clustering and classification algorithms.…”

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

“…Multi-target tracking with radar is also a hot issue in intelligent transportation research [4][5][6]. By tracking passing vehicles, risky driving behavior can be predicted and an early warning signal can be issued [7,8]. Vehicle tracking helps to reduce the occurrence of traffic accidents, and also helps the development of intelligent transportation [9].…”