Improved Multi-Bernoulli Filter for Extended Stealth Targets Tracking Based on Sub-Random Matrices

Zong, Peng; Barbary, Mohamed

doi:10.1109/jsen.2015.2499268

Cited by 35 publications

(46 citation statements)

References 22 publications

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“…As a result, for an isolated clustering, due to the inclusion of inter-cluster association pair(s) (bad association pair(s)), all the inter-cluster fusion hypothesis have a negligible contribution to the GMB density according to (26), and they are exactly what we want to find and truncate. Discarding all the intercluster hypotheses, denoted by D = {(I 1 , θ) ∈ F(L 1 ) × Θ : (I 1 , θ) ∩ P = ∅)}, the un-normalized GMB density given by (17) can be approximated by…”

Section: Definitionmentioning

confidence: 99%

“…Compared with PHD [10], [11] and CPHD [12]- [15] filters, the Multi-Bernoulli (MB) filter [15], [16] can be more efficient and accurate in problems that require object individual existence probabilities. The performance of the MB filters has been well demonstrated in a wide range of practical monitoring problems such as radar tracking [17], video and audio tracking [16], [18], sensor control [19], [20], and acoustic sensor tracking [21]. In addition, a number of novel extensions of the MB recursion have also been investigated.…”

Section: Introductionmentioning

confidence: 99%

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Computationally Efficient Distributed Multi-Sensor Fusion With Multi-Bernoulli Filter

Wang

et al. 2020

IEEE Trans. Signal Process.

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This paper proposes a computationally efficient algorithm for distributed fusion in a sensor network in which multi-Bernoulli (MB) filters are locally running in every sensor node for multi-target tracking. The generalized Covariance Intersection (GCI) fusion rule is employed to fuse multiple MB random finite set densities. The fused density comprises a set of fusion hypotheses that grow exponentially with the number of Bernoulli components. Thus, GCI fusion with MB filters can become computationally intractable in practical applications that involve tracking of even a moderate number of objects. In order to accelerate the multi-sensor fusion procedure, we derive a theoretically sound approximation to the fused density. The number of fusion hypotheses in the resulting density is significantly smaller than the original fused density. It also has a parallelizable structure that allows multiple clusters of Bernoulli components to be fused independently. By carefully clustering Bernoulli components into isolated clusters using the GCI divergence as the distance metric, we propose an alternative to build exactly the approximated density without exhaustively computing all the fusion hypotheses. The combination of the proposed approximation technique and the fast clustering algorithm can enable a novel and fast GCI-MB fusion implementation. Our analysis shows that the proposed fusion method can dramatically reduce the computational and memory requirements with small bounded L1-error. The Gaussian mixture implementation of the proposed method is also presented. In various numerical experiments, including a challenging scenario with up to forty objects, the efficacy of the proposed fusion method is demonstrated. arXiv:1906.07991v1 [eess.SY]

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Section: Definitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computationally Efficient Distributed Multi-Sensor Fusion With Multi-Bernoulli Filter

Wang

et al. 2020

IEEE Trans. Signal Process.

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“…Thus, such a sensor is subject to a certain degree of uncertainty, resulting in relatively high miss-detections and false alarm rates. This could become exacerbated in multitarget tracking (MTT) scenarios, where the mappings between the targets and the measurements are unknown [3], [4]. The number of targets in the surveillance area of interest is also not known a priori in a typical MTT problem.…”

Section: Introductionmentioning

confidence: 99%

Multi-Sensor Multi-Target Tracking Using Domain Knowledge and Clustering

Shin

Tsourdos

2018

IEEE Sensors J.

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This paper proposes a novel joint multi-target tracking and track maintenance algorithm over a sensor network. Each sensor runs a local joint probabilistic data association (JPDA) filter using only its own measurements. Unlike the original JPDA approach, the proposed local filter utilises the detection amplitude as domain knowledge to improve the estimation accuracy. In the fusion stage, the DBSCAN clustering in conjunction with statistical test is proposed to group all local tracks into several clusters. Each generated cluster represents the local tracks that are from the same target source and the global estimation of each cluster is obtained by the generalized covariance intersection (GCI) algorithm. Extensive simulation results clearly confirms the effectiveness of the proposed multisensor multi-target tracking algorithm.

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“…However, Lan’s approaches are only applicable to a single-target scene without clutter. Besides, these approaches, to the best of our knowledge, have not been used in a framework for tracking an unknown number of extended targets in the presence of missed detections and clutter except [ 28 ], where an extended CBMeMBer filter is proposed for NETT problem. However, the approach of [ 28 ] only applies to non-maneuvering tracking scenes.…”

Section: Introductionmentioning

confidence: 99%

Improved GGIW-PHD filter for maneuvering non-ellipsoidal extended targets or group targets tracking based on sub-random matrices

2018

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For non-ellipsoidal extended targets and group targets tracking (NETT and NGTT), using an ellipsoid to approximate the target extension may not be accurate enough because of the lack of shape and orientation information. In consideration of this, we model a non-ellipsoidal extended target or target group as a combination of multiple ellipsoidal sub-objects, each represented by a random matrix. Based on these models, an improved gamma Gaussian inverse Wishart probability hypothesis density (GGIW-PHD) filter is proposed to estimate the measurement rates, kinematic states, and extension states of the sub-objects for each extended target or target group. For maneuvering NETT and NGTT, a multi-model (MM) approach based GGIW-PHD (MM-GGIW-PHD) filter is proposed. The common and the individual dynamics of the sub-objects belonging to the same extended target or target group are described by means of the combination between the overall maneuver model and the sub-object models. For the merging of updating components, an improved merging criterion and a new merging method are derived. A specific implementation of prediction partition with pseudo-likelihood method is presented. Two scenarios for non-maneuvering and maneuvering NETT and NGTT are simulated. The results demonstrate the effectiveness of the proposed algorithms.

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Improved Multi-Bernoulli Filter for Extended Stealth Targets Tracking Based on Sub-Random Matrices

Cited by 35 publications

References 22 publications

Computationally Efficient Distributed Multi-Sensor Fusion With Multi-Bernoulli Filter

Computationally Efficient Distributed Multi-Sensor Fusion With Multi-Bernoulli Filter

Multi-Sensor Multi-Target Tracking Using Domain Knowledge and Clustering

Improved GGIW-PHD filter for maneuvering non-ellipsoidal extended targets or group targets tracking based on sub-random matrices

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