A Robust Tracking Algorithm Based on Modified Generalized Probability Data As-sociation for Wireless Sensor Network
ABSTARCT :
Wireless sensor network (WSN) is composed of many micro sensor nodes, and the localization technology is one of the most important applications of WSN technology. At present, many positioning algorithms have high position-ing accuracy in line-of-sight (LOS) environment, but poor positioning accuracy in non-line-of-sight (NLOS) environ-ment. In this paper, we propose a modified generalized probability data association algorithm based on arrival of time (TOA). We divided the range measurements into N different groups, and each group obtained the corre-sponding position estimation, model probabilities and covariance matrix of the mobile node through IMM-EKF. We used model probability and hypothesis test to perform NLOS identification for N groups, in which the model probability provided by each group was used for the first NLOS identification, and the innovation and innovation covariance matrix were used for the second NLOS identi-fication in the hypothesis test. Position estimation con-taminated by NLOS error is discarded. The correct position estimation is weighted with the corresponding association probability. The simulation and experimental results show that the proposed algorithm can mitigate the influence of NLOS errors and achieve higher localization accuracy when compared with the existing methods.
EXISTING SYSTEM :
? Due to various constraints, existing localization systems, such as GPS, cannot be used for the localization of wireless sensor nodes.
? Majority of the existing localization algorithms may be classified as ranged-based or range-free depending upon whether the algorithm uses distance estimation or some other information for estimating the node locations.
? The distances between dumb nodes and the beacon nodes are usually determined by adding some additional hardware to the nodes or by using the existing radio communication facility on the sensor nodes.
? As a result, one-way time-of-arrival measurements using RF signals are not considered as a choice for the existing and near future sensor hardware.
DISADVANTAGE :
? In this paper, the NLOS tracer method is proposed to solve this problem to improve the robustness of the probabilistic data association algorithm.
? It not only solves the problem of the cooperative localization using multiple source nodes, but also improves the localization performance compared with the classic EKF.
? Almost all IMM algorithms need to presuppose NLOS statistical errors to solve the NLOS interference problem, but in practice, the NLOS statistical errors are unknown.
? Due to there being many obstacles, it is difficult to provide accurate localization indoors. Applying wireless sensor network technology to indoor localization can solve the problem of indoor localization.
PROPOSED SYSTEM :
• A number of algorithms and techniques based upon different characteristics and properties of sensor nodes have already been proposed for this purpose.
• An improved version of the MDS-based localization algorithm, called MDS-MAP(P), which is short for MDS-MAP using patches of relative maps, has also been proposed.
• In addition to the algorithms described above, a number of localization algorithms have been proposed such as Recursive Position Estimation and Directed Position Estimation.
• Along the way, new techniques and algorithms are being proposed and developed for various layers in the sensor networks.
ADVANTAGE :
? Due to there being many obstacles, it is difficult to provide accurate localization indoors. Applying wireless sensor network technology to indoor localization can solve the problem of indoor localization.
? When the probability of the NLOS errors was relatively large, the proposed algorithm had a better localization performance than the EKF, IMM-EKF, and MPDA.
? The proposed algorithm had a higher positioning accuracy than the EKF, IMM-EKF, and MPDA with about 65.83%, 53.01%, and 18.56%, respectively, on average.
? The more NLOS data that is used, the less accurate the positioning is. According to the idea of data fusion, combining different positioning methods is also a common method to improving positioning accuracy.
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