Multi-Bernoulli filter for target tracking with multi-static Doppler only measurement

Ma, Liang; Kim, Du Yong; Xue, Kaiping

doi:10.1016/j.sigpro.2014.09.013

Cited by 21 publications

(7 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the selected receives, the posterior density is updated sequentially. For more details of the multi-Bernoulli filter with sequential update scheme, the reader is referred to [31]. The geometry of a single transmitter-target-receiver pair is illustrated in Figure 3, where the locations of the transmitter, the receiver and the target are false(xT,yTfalse), false(xR,yRfalse) and false(x,yfalse), respectively.…”

Section: Simulationsmentioning

confidence: 99%

Multi-Objective Optimization Based Multi-Bernoulli Sensor Selection for Multi-Target Tracking

Zhu

Wang

Liang

2019

Sensors

View full text Add to dashboard Cite

This paper presents a novel multi-objective optimization based sensor selection method for multi-target tracking in sensor networks. The multi-target states are modelled as multi-Bernoulli random finite sets and the multi-Bernoulli filter is used to propagate the multi-target posterior density. The proposed method is designed to select the sensor that provides the most reliable cardinality estimate, since more accurate cardinality estimate indicates more accurate target states. In the multi-Bernoulli filter, the updated multi-target density is a multi-Bernoulli random finite set formed by a union of legacy tracks and measurement-updated tracks. The legacy track and the measurement-updated track have different theoretical and physical meanings, and hence these two kinds of tracks are considered separately in the sensor management problem. Specifically, two objectives are considered: (1) maximizing the mean cardinality of the measurement-updated tracks, (2) minimizing the cardinality variance of the legacy tracks. Considering the conflicting objectives simultaneously is a multi-objective optimization problem. Tradeoff solutions between two conflicting objectives will be derived. Theoretical analysis and examples show that the proposed approach is effective and direct. The performance of the proposed method is demonstrated using two scenarios with different levels of observability of targets in the passive sensor network.

show abstract

Section: Simulationsmentioning

confidence: 99%

Multi-Objective Optimization Based Multi-Bernoulli Sensor Selection for Multi-Target Tracking

Zhu

Wang

Liang

2019

Sensors

View full text Add to dashboard Cite

show abstract

“…Filter. Sequential update has been widely used and verified to be a good approximation for information fusion of multiple sensors [3,17]. Here, we introduce the sequential multi-Bernoulli filter in the SMC implementation which is proposed in [17].…”

Section: Sequential Multi-bernoullimentioning

confidence: 99%

Multitarget Tracking with Spatial Nonmaximum Suppressed Sensor Selection

Xue

2015

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Multitarget tracking is one of the most important applications of sensor networks, yet it is an extremely challenging problem since multisensor multitarget tracking itself is nontrivial and the difficulty is further compounded by sensor management. Recently, random finite set based Bayesian framework has opened doors for multitarget tracking with sensor management, which is modelled in the framework of partially observed Markov decision process (POMDP). However, sensor management posed as a POMDP is in essence a combinatorial optimization problem which is NP-hard and computationally unacceptable. In this paper, we propose a novel sensor selection method for multitarget tracking. We first present the sequential multi-Bernoulli filter as a centralized multisensor fusion scheme for multitarget tracking. In order to perform sensor selection, we define the hypothesis information gain (HIG) of a sensor to measure its information quantity when the sensor is selected alone. Then, we propose spatial nonmaximum suppression approach to select sensors with respect to their locations and HIGs. Two distinguished implementations have been provided using the greedy spatial nonmaximum suppression. Simulation results verify the effectiveness of proposed sensor selection approach for multitarget tracking.

show abstract

“…Assume that r k|k −1 (x k , γ k , λ k ) can be expressed in a compact form as follows (41) Substituting (34) and (41) into (20)-(21) and using EKF technique to address the first sensor measurement yields…”

Section: Gaussian Mixture Implementations To the Multi-sensor Mmbfmentioning

confidence: 99%

“…In [26], the problem of joint detection and tracking of a target using multi-static Doppler-only measurements has been investigated and a multi-sensor Bernoulli particle filter has been developed. The Bernoulli filter has been extended to track multiple targets, which is also known as the cardinality balanced multi-target multiBernoulli filter [27][28][29][30][31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

RSS-based joint detection and tracking in mixed LOS and NLOS environments

Jia

2015

Digital Signal Processing

View full text Add to dashboard Cite

Multi-Bernoulli filter for target tracking with multi-static Doppler only measurement

Cited by 21 publications

References 27 publications

Multi-Objective Optimization Based Multi-Bernoulli Sensor Selection for Multi-Target Tracking

Multi-Objective Optimization Based Multi-Bernoulli Sensor Selection for Multi-Target Tracking

Multitarget Tracking with Spatial Nonmaximum Suppressed Sensor Selection

RSS-based joint detection and tracking in mixed LOS and NLOS environments

Contact Info

Product

Resources

About