Multi-Sensor Joint Adaptive Birth Sampler for Labeled Random Finite Set Tracking

Trezza, Anthony; Bucci, Donald J.; Varshney, Pramod K.

doi:10.1109/tsp.2022.3151553

Cited by 24 publications

(8 citation statements)

References 50 publications

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“…(a) Initialize the state and covariance of target as (24). (b) Select x i k,0 and P i k,0 as information pairs and exchange newly information pairs between neighboring nodes.…”

Section: The Analysis Of Estimate Error Boundednessmentioning

confidence: 99%

“…However, limited by the computational complexity in DA algorithm, DA based MTT algorithms endure huge computational burden when it encounters the scenario with high clutter density and target density. Then the RFS based MTT algorithm is proposed by combing the RFS theory and MTT algorithm [24]. The RFS based MTT algorithm uses the random finite set to describe multi-target state and measurement.…”

Section: Introductionmentioning

confidence: 99%

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Distributed robust cooperative tracking against multiple maneuvering targets based on random finite set

Zhang

Dong

et al. 2022

IET Control Theory & Appl

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This paper considers the distributed multi-target tracking problem with mobile sensor network. Based on the random finite set and consensus methods, a distributed multi-target tracking algorithm is designed by local filtering stage and consensus fusion stage. In the local filtering stage, the state and measurement information of multi-target are described by random finite set. Each node in the sensor network obtains the state estimates of multi-target individually by applying the probability hypothesis density filtering algorithm. To reduce the computational complexity, the Jacobian matrix and Hessian matrix are used to calculate the priori probability density. With the local neighboring interactions, the estimate results of multi-target system are obtained after the consensus fusion stage. Besides, the stochastic boundedness of the estimation error of the distributed multi-target tracking algorithm is proved by constructing a stochastic process. In the final, a simulation example is given to verify the multi-target tracking performance of the proposed algorithm.

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“…(a) Initialize the state and covariance of target as (24). (b) Select x i k,0 and P i k,0 as information pairs and exchange newly information pairs between neighboring nodes.…”

Section: The Analysis Of Estimate Error Boundednessmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distributed robust cooperative tracking against multiple maneuvering targets based on random finite set

Zhang

Dong

et al. 2022

IET Control Theory & Appl

View full text Add to dashboard Cite

show abstract

“…Regardless of some recent definitions of disvergence/distances for two distributions of different dimensions or domains [35], there still lacks a proper distance/divergence for the LRFS densities with different, discrete labels. As such, both (14) and (15) can not be directly applied for the labeled RFS density distributions in general. Yet, this has been often violated in the literature.…”

Section: Phd-aa Consistencymentioning

confidence: 99%

“…To evaluate the degree of fit, one may use the KL divergence which, however, does not admits analytical solution for the GMs. For the sake of computational efficiency, one may consider the Cauchy-Schwarz divergence [38] and the ISE metric as given in (14), both of which allow analytical calculation for GMs. We consider the latter only, which complies with the BFoM as expressed by (14).…”

Section: A Minimizing Ise Of Gms Via Reweightingmentioning

confidence: 99%

“…For the sake of computational efficiency, one may consider the Cauchy-Schwarz divergence [38] and the ISE metric as given in (14), both of which allow analytical calculation for GMs. We consider the latter only, which complies with the BFoM as expressed by (14). Formally speaking, the goal of our approach is to determine the new weight for each L-GC at the local sensor in the following BFoM sense…”

Section: A Minimizing Ise Of Gms Via Reweightingmentioning

confidence: 99%

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Distributed Multisensor Multitarget Tracking Algorithm with Time-Offset Registration

Chen

Wang

et al. 2020

JNWPU

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In multisensor systems, the signal processing delay, measurement acquisition delay, and other factors will lead to imprecisely time-stamped measurements, namely, the problem of time-offset. To deal with the measurement time offsets in distributed multisensor systems, a distributed multisensor multitarget tracking algorithm with time-offset registration is proposed. The local processors track multiple targets in the presence of false alarms and missed detections based on the joint probabilistic data association (JPDA) algorithm and the extended Kalman filter (EKF), providing the time-biased local tracks. In the global processor, in allusion to the global track accuracy degradation introduced by the time offsets of local tracks, the equivalent measurements are firstly constructed based on local tracks by using the inverse Kalman filter. The pseudo-measurement equation of time offset for constant velocity targets is derived and the pseudo-measurement calculation method is presented. Then, the pseudo-measurement based relative time-offset estimation algorithm is presented, by using the recursive least squares estimation (RLSE) and the Kalman filter (KF) to jointly estimate the state in space and time domains, respectively. Finally, a framework of distributed multisensor multitarget tracking with time-offset registration is presented, where the time-varying relative time-offset estimation and compensation, 'equivalent measurement to global track' association, and global track update are included. Simulations for multisensor multitarget tracking in the presence of false alarms and missed detections are conducted, demonstrating that the present algorithm effectively improves the accuracy of fused global tracks.

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A variational Bayesian approach for formation target tracking

Zhang,

Liang,

Zhu

et al. 2024

Aerospace Science and Technology

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Multi-Sensor Joint Adaptive Birth Sampler for Labeled Random Finite Set Tracking

Cited by 24 publications

References 50 publications

Distributed robust cooperative tracking against multiple maneuvering targets based on random finite set

Distributed robust cooperative tracking against multiple maneuvering targets based on random finite set

Distributed Multisensor Multitarget Tracking Algorithm with Time-Offset Registration

A variational Bayesian approach for formation target tracking

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