scholarly journals Variational Bayesian Based Adaptive Shifted Rayleigh Filter for Bearings-Only Tracking in Clutters

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1512 ◽  
Author(s):  
Jing Hou ◽  
Yan Yang ◽  
Tian Gao
Keyword(s):  
Target Tracking ◽  
Data Association ◽  
Tracking Performance ◽  
Computation Complexity ◽  
Variational Bayesian ◽  
Target State ◽  
Scenarios Simulation ◽  
Simulation Results

This paper considers bearings-only target tracking in clutters with uncertain clutter probability. The traditional shifted Rayleigh filter (SRF), which assumes known clutter probability, may have degraded performance in challenging scenarios. To improve the tracking performance, a variational Bayesian-based adaptive shifted Rayleigh filter (VB-SRF) is proposed in this paper. The target state and the clutter probability are jointly estimated to account for the uncertainty in clutter probability. Performance of the proposed filter is evaluated by comparing with SRF and the probability data association (PDA)-based filters in two scenarios. Simulation results show that the proposed VB-SRF algorithm outperforms the traditional SRF and PDA-based filters especially in complex adverse scenarios in terms of track continuity, track accuracy and robustness with a little higher computation complexity.

Optimal Deployment for Mobile Target Tracking in Sensor Networks

2012 ◽  
Vol 468-471 ◽  
pp. 1657-1660
Author(s):  
Ying Chi Mao
Keyword(s):  
Target Tracking ◽  
Tracking Performance ◽  
Wireless Sensor ◽  
Sensor Deployment ◽  
Surveillance Systems ◽  
Challenging Problem ◽  
Irregular Pattern ◽  
Optimal Deployment ◽  
Mobile Target ◽  
Simulation Results

Mobile target tracking is a key application of wireless sensor network-based surveillance systems. Sensor deployment is an important factor in tracking performance and remains a challenging problem. In this paper, we address the problem of optimal sensor deployment for mobile target tracking. We analyze the tracking performance of three patterns. Simulation results demonstrate that the irregular pattern outperforms the other two patterns.

scholarly journals A Multi-Target Tracking and Detection Algorithm for Wireless Sensor Networks

2021 ◽  
Vol 15 ◽  
pp. 661-665
Author(s):  
Gang Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Target Tracking ◽  
Detection Algorithm ◽  
Wireless Sensor ◽  
Main Function ◽  
Multiple Target ◽  
Target State ◽  
Multi Target Tracking ◽  
Simulation Results

There are a large number of sensor nodes in wireless sensor network, whose main function is to process data scientifically, so that it can better sense and cooperate. In the network coverage, it can comprehensively collect the main information of the monitoring object, and send the monitoring data through short-range wireless communication to the gateway. Although there are many applications in WSNs, a multi-Target tracking and detection algorithm and the optimization problem of the wireless sensor networks are discussed in this paper. It can be obviously seen from the simulation results that this node cooperative program using particle CBMeMBer filtering algorithm can perfectly handle multi-target tracking, even if the sensor model is seriously nonlinear. Simulation results show that the tracking - forecasting data association scheme applying GM-CBMeMBer, which is proposed in this paper, runs well in identifying multiple target state, and can improve the estimation accuracy of multiple target state.

Modified Joint Probability Data Association Algorithm Avoiding Track Coalescence

2012 ◽  
Vol 433-440 ◽  
pp. 2298-2303 ◽  
Author(s):  
Song Lin Chen ◽  
Yi Bing Xu
Keyword(s):  
Small Angle ◽  
Data Association ◽  
Joint Probability ◽  
Tracking Performance ◽  
Probabilistic Data ◽  
Multiple Targets ◽  
Probabilistic Data Association ◽  
Simulation Results ◽  
Exclusive Measurement ◽  
Association Algorithm

Joint Probabilistic Data Association has proven to be effective in tracking multiple targets from measurements amidst clutter and missed detections. But the traditional Joint Probabilistic Data Association algorithm will cause track coalescence when the targets are parallel neighboring or small-angle crossing. To avoid track coalescence, a modified Joint Probabilistic Data Association algorithm is proposed in this paper. An exclusive measurement is defined for every target in the new algorithm. The exclusive measurement of a target is one measurement which associates with the target and has the maximum associated probability. The associated events of the exclusive measurement with other targets will be pruned, which resists two or more targets share the same measurement as a key measurement and avoids track coalescence. The simulation results show that the new algorithm can effectively solve track coalescence problem in all kinds of scenarios and keep a high tracking performance.

scholarly journals Tracking of Multiple Closely Spaced Extended Targets Based on Prediction-Driven Measurement Sub-Partitioning Algorithm

2020 ◽  
Vol 10 (14) ◽  
pp. 5004
Author(s):  
Lifan Sun ◽  
Haofang Yu ◽  
Zhumu Fu ◽  
Zishu He ◽  
Fazhan Tao
Keyword(s):  
Target Tracking ◽  
Tracking Performance ◽  
Target Number ◽  
Computational Burden ◽  
Extended Target ◽  
Accuracy Of Measurement ◽  
Partitioning Problem ◽  
Simulation Results ◽  
Partitioning Algorithm ◽  
Partitioning Algorithms

For multiple extended target tracking, the accuracy of measurement partitioning directly affects the target tracking performance, so the existing partitioning algorithms tend to use as many partitions as possible to obtain accurate estimates of target number and states. Unfortunately, this may create an intolerable computational burden. What is worse is that the measurement partitioning problem of closely spaced targets is still challenging and difficult to solve well. In view of this, a prediction-driven measurement sub-partitioning (PMS) algorithm is first proposed, in which target predictions are fully utilized to determine the clustering centers for obtaining accurate partitioning results. Due to its concise mathematical forms and favorable properties, redundant measurement partitions can be eliminated so that the computational burden is largely reduced. More importantly, the unreasonable target predictions may be marked and replaced by PMS for solving the so-called cardinality underestimation problem without adding extra measurement partitions. PMS is simple to implement, and based on it, an effective multiple closely spaced extended target tracking approach is easily obtained. Simulation results verify the benefit of what we proposed—it has a much faster tracking speed without degrading the performance compared with other approaches, especially in a closely spaced target tracking scenario.

Data Association Method for Radar Network Based on Time Restraint

2012 ◽  
Vol 157-158 ◽  
pp. 415-418
Author(s):  
Shen Shen Wang ◽  
Wan Fang Che ◽  
Jin Fu Feng ◽  
Fang Nian Wang ◽  
Yun Bai
Keyword(s):  
Target Tracking ◽  
Real Time ◽  
Tracking System ◽  
Data Association ◽  
Time Requirement ◽  
Association Model ◽  
Radar Network ◽  
Multi Target Tracking ◽  
Simulation Results ◽  
Association Method

Data association is one of the most important issues in multi-target tracking for radar network. In order to meet the high real-time requirement of the multi-target tracking system in the future, a data association method based on time restraint is presented. Firstly, the modified association probability matrix between the observations and tracks is calculated, and the optimal association model is established. Then, the time restraint based auction algorithm is proposed to solve the association issue. The design and flow of this algorithm is offered, and the simulation of the method is designed. Simulation results demonstrate that the proposed method has an ideal association performance in the restrained time.

scholarly journals Robust Multisensor MeMBer Filter for Multiple Extended-Target Tracking

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xiaoke Lu ◽  
Zhiguo Zhang ◽  
Qing Li ◽  
Jinping Sun
Keyword(s):  
Target Tracking ◽  
Likelihood Function ◽  
Target State ◽  
Extended Target ◽  
Single Target ◽  
Simulation Results

This paper develops a robust extended-target multisensor multitarget multi-Bernoulli (ET-MS-MeMBer) filter for enhancing the unsatisfactory quality of measurement partitions arising in the classical ET-MS-MeMBer filter due to increased clutter intensities. Specifically, the proposed method considers the influence of the clutter measurement set by introducing the ratio of the target likelihood to the clutter likelihood. With the constraint of the clutter measurement set, it can obtain better multisensor measurement partitioning results under the original two-step greedy partitioning mechanism. Subsequently, the single-target multisensor likelihood function for the clutter case is derived. Simulation results reveal a favorable comparison to the ET-MS-MeMBer filter in terms of accuracy in estimating the target cardinality and target state under conditions with increased clutter intensities.

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