Monte Carlo data association for multiple target tracking

2001 ◽  
Author(s):  
R. Karlsson
Keyword(s):  
Monte Carlo ◽  
Target Tracking ◽  
Data Association ◽  
Multiple Target Tracking ◽  
Multiple Target ◽  
Monte Carlo Data

scholarly journals Gating Techniques for Rao-Blackwellized Monte Carlo Data Association Filter

2014 ◽  
Vol 2014 ◽  
pp. 1-11
Author(s):  
Yazhao Wang ◽  
Peini Zhang
Keyword(s):  
Monte Carlo ◽  
Data Association ◽  
Multiple Target Tracking ◽  
Time Cost ◽  
Multiple Target ◽  
Monte Carlo Data ◽  
Tracking Ability ◽  
Obvious Benefit ◽  
Measurement Uncertainties ◽  
Simulation Results

This paper studies the Rao-Blackwellized Monte Carlo data association (RBMCDA) filter for multiple target tracking. The elliptical gating strategies are redesigned and incorporated into the framework of the RBMCDA filter. The obvious benefit is the reduction of the time cost because the data association procedure can be carried out with less validated measurements. In addition, the overlapped parts of the neighboring validation regions are divided into several separated subregions according to the possible origins of the validated measurements. In these subregions, the measurement uncertainties can be taken into account more reasonably than those of the simple elliptical gate. This would help to achieve higher tracking ability of the RBMCDA algorithm by a better association prior approximation. Simulation results are provided to show the effectiveness of the proposed gating techniques.

scholarly journals Statistical consistency of the data association problem in multiple target tracking

2011 ◽  
Vol 5 (0) ◽  
pp. 1227-1275 ◽  
Author(s):  
Curtis B. Storlie ◽  
Jan Hannig ◽  
Thomas C.M. Lee
Keyword(s):  
Target Tracking ◽  
Data Association ◽  
Multiple Target Tracking ◽  
Multiple Target ◽  
Statistical Consistency

scholarly journals Passive Sonar Multiple-Target Tracking with Nonlinear Doppler and Bearing Measurements Using Multiple Sensors

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xiaohua Li ◽  
Bo Lu ◽  
Wasiq Ali ◽  
Jun Su ◽  
Haiyan Jin
Keyword(s):  
Target Tracking ◽  
Tracking System ◽  
Data Association ◽  
Measurement Data ◽  
Target Range ◽  
Multiple Target Tracking ◽  
Multiple Target ◽  
Multiple Sensors ◽  
Passive Sensors ◽  
Passive Measurements

The major advantage of the passive multiple-target tracking is that the sonars do not emit signals and thus they can remain covert, which will reduce the risk of being attacked. However, the nonlinearity of the passive Doppler and bearing measurements, the range unobservability problem, and the measurement to target data association uncertainty make the passive multiple-target tracking problem challenging. To deal with the target to measurement data association uncertainty problem from multiple sensors, this paper proposed a batch recursive extended Rauch-Tung-Striebel smoother- (RTSS-) based probabilistic multiple hypothesis tracker (PMHT) algorithm, which can effectively handle a large number of passive measurements including clutters. The recursive extended RTSS which consists of a forward filter and a backward smoothing is used to deal with the nonlinear Doppler and bearing measurements. The target range unobservability problem is avoided due to using multiple passive sensors. The simulation results show that the proposed algorithm works well in a passive multiple-target tracking system under dense clutter environment, and its computing cost is low.

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