Centralized multi-sensor multi-target tracking with labeled random finite sets

Wei, Baishen; Nener, Brett; Liu, Weifeng; Ma, Liang

doi:10.1109/iccais.2016.7822440

Cited by 16 publications

(14 citation statements)

References 27 publications

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“…The random finite set (RFS) [1] has received much attention in the multi-target filtering domain due to its superiority in avoiding complicated data association steps [2]- [12]. Under the RFS framework, the target state estimation is transformed into a set-valued estimation problem.…”

Section: Introductionmentioning

confidence: 99%

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A Novel MS-MeMBer Filter for Extended Targets Tracking

Zhang

Sun

et al. 2020

IEEE Access

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Conventional multi-sensor multi-target multi-Bernoulli (MS-MeMBer) filters are based on the assumption that each target produces at most one measurement per time step. However, this assumption is not always reasonable in practice as an extended target can generate multiple measurements per step due to the recent improvement in the sensor resolution. In this case, a potential estimation bias may occur in the current MS-MeMBer filters. Therefore, a novel extended target MS-MeMBer filter and its Gaussian inverse Wishart mixture implementation are given in this paper. Specifically, we modify the update process of the MS-MeMBer filter by assuming that the generation of extended target measurements follows an approximate Poisson-Body model. Simulation results validate that the proposed filter can effectively estimate the shape and position of the extended target. INDEX TERMS Multi-sensor multi-target multi-Bernoulli filter, extended target, approximate Poissonbody, Gaussian inverse Wishart.

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Section: Introductionmentioning

confidence: 99%

“…In [11], Saucan introduced a multi-sensor multitarget multi-Bernoulli (MS-MeMBer) filter via a Gaussian mixture (GM) implementation. Similarly, a generalization of the GLMB filter for the multi-sensor case, named multi-sensor GLMB (MS-GLMB) filter, was proposed in [12].…”

Section: Introductionmentioning

confidence: 99%

A Novel MS-MeMBer Filter for Extended Targets Tracking

Zhang

Sun

et al. 2020

IEEE Access

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“…Since the problem of tracking with Doppler measurements in this work requires multiple sensors, both the Murty’s algorithm and Gibbs sampler implementation should be considered. However, the implementation of the two sensor GLMB filter developed in Reference [56] using Murty’s algorithm showed that it has a cubic complexity in the product of the number of measurements from the sensors. Based on the extension of the Gibbs sampler implementation to multiple sensors proposed in Reference [52], Gibbs sampling is chosen as the most appropriate implementation for this problem.…”

Section: Introductionmentioning

confidence: 99%

Tracking Multiple Targets from Multistatic Doppler Radar with Unknown Probability of Detection

Nguyen

2019

Sensors

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The measurements from multistatic radar systems are typically subjected to complicated data association, noise corruption, missed detection, and false alarms. Moreover, most of the current multistatic Doppler radar-based approaches in multitarget tracking are based on the assumption of known detection probability. This assumption can lead to biased or even complete corruption of estimation results. This paper proposes a method for tracking multiple targets from multistatic Doppler radar with unknown detection probability. A closed form labeled multitarget Bayes filter was used to track unknown and time-varying targets with unknown probability of detection in the presence of clutter, misdetection, and association uncertainty. The efficiency of the proposed algorithm was illustrated via numerical simulation examples.

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“…In [21], the multisensor CPHD (MS-CPHD) filter was introduced, and the multisensor MeMBer (MS-MeMBer) filter was proposed in [22]. Based on the labeled RFS, the centralized multisensor δ-GLMB (MS-δ-GLMB) with extended association maps was introduced in [23].…”

Section: Introductionmentioning

confidence: 99%

Multisensor RFS Filters for Unknown and Changing Detection Probability

Zhang

Sun

2019

Electronics

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The detection probability is an important parameter in multisensor multitarget tracking. The existing multisensor multi-Bernoulli (MS-MeMBer) filter and multisensor cardinalized probability hypothesis density (MS-CPHD) filter require that detection probability is a priori. However, in reality, the value of the detection probability is constantly changing due to the influence of sensors, targets, and other environmental characteristics. Therefore, to alleviate the performance deterioration caused by the mismatch of the detection probability, this paper applies the inverse gamma Gaussian mixture (IGGM) distribution to both the MS-MeMBer filter and the MS-CPHD filter. Specifically, the feature used for detection is assumed to obey the inverse gamma distribution and is statistically independent of the target’s spatial position. The feature is then integrated into the target state to iteratively estimate the target detection probability as well as the motion state. The experimental results demonstrate that the proposed methods can achieve a better filtering performance in scenarios with unknown and changing detection probability. It is also shown that the distribution of the sensors has a vital influence on the filtering accuracy, and the filters perform better when sensors are dispersed in the monitoring area.

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Centralized multi-sensor multi-target tracking with labeled random finite sets

Cited by 16 publications

References 27 publications

A Novel MS-MeMBer Filter for Extended Targets Tracking

A Novel MS-MeMBer Filter for Extended Targets Tracking

Tracking Multiple Targets from Multistatic Doppler Radar with Unknown Probability of Detection

Multisensor RFS Filters for Unknown and Changing Detection Probability

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