scholarly journals Extended Target Marginal Distribution Poisson Multi-Bernoulli Mixture Filter

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5387
Author(s):  
Haocui Du ◽  
Weixin Xie
Keyword(s):  
Target Tracking ◽  
Marginal Distribution ◽  
Computing Time ◽  
Validity And Reliability ◽  
Unknown Number ◽  
Extended Target ◽  
Simulation Results ◽  
Existence Probability

The existence of clutter, unknown measurement sources, unknown number of targets, and undetected probability are problems for multi-extended target tracking, to address these problems; this paper proposes a gamma-Gaussian-inverse Wishart (GGIW) implementation of a marginal distribution Poisson multi-Bernoulli mixture (MD-PMBM) filter. Unlike existing multiple extended target tracking filters, the GGIW-MD-PMBM filter computes the marginal distribution (MD) and the existence probability of each target, which can shorten the computing time while maintaining good tracking results. The simulation results confirm the validity and reliability of the GGIW-MD-PMBM filter.

scholarly journals Adaptive Target Birth Intensity for ET-PHD Filter

2018 ◽  
Vol 176 ◽  
pp. 03010
Author(s):  
Lu Miao ◽  
Xin-xi Feng ◽  
Luo-jia Chi
Keyword(s):  
Target Tracking ◽  
Gaussian Mixture ◽  
Tracking Algorithm ◽  
Tracking Problem ◽  
Probability Hypothesis Density ◽  
Probability Hypothesis Density Filter ◽  
Target Probability ◽  
Extended Target ◽  
Density Filter ◽  
Simulation Results

An adaptive tracking algorithm based on Extended target Probability Hypothesis Density (ETPHD) filter is proposed for extended target tracking problem with priori unknown target birth intensity.The algorithm is implemented by gaussian mixture, where the target birth intensity is generated by measurement-driven, and the persistent and the newborn targets intensity are respectively predicted and updated. The simulation results show that the proposed algorithm improves the performance of the probability hypothesis density filter in the extended target tracking.

scholarly journals Tracking of Multiple Closely Spaced Extended Targets Based on Prediction-Driven Measurement Sub-Partitioning Algorithm

2020 ◽  
Vol 10 (14) ◽  
pp. 5004
Author(s):  
Lifan Sun ◽  
Haofang Yu ◽  
Zhumu Fu ◽  
Zishu He ◽  
Fazhan Tao
Keyword(s):  
Target Tracking ◽  
Tracking Performance ◽  
Target Number ◽  
Computational Burden ◽  
Extended Target ◽  
Accuracy Of Measurement ◽  
Partitioning Problem ◽  
Simulation Results ◽  
Partitioning Algorithm ◽  
Partitioning Algorithms

For multiple extended target tracking, the accuracy of measurement partitioning directly affects the target tracking performance, so the existing partitioning algorithms tend to use as many partitions as possible to obtain accurate estimates of target number and states. Unfortunately, this may create an intolerable computational burden. What is worse is that the measurement partitioning problem of closely spaced targets is still challenging and difficult to solve well. In view of this, a prediction-driven measurement sub-partitioning (PMS) algorithm is first proposed, in which target predictions are fully utilized to determine the clustering centers for obtaining accurate partitioning results. Due to its concise mathematical forms and favorable properties, redundant measurement partitions can be eliminated so that the computational burden is largely reduced. More importantly, the unreasonable target predictions may be marked and replaced by PMS for solving the so-called cardinality underestimation problem without adding extra measurement partitions. PMS is simple to implement, and based on it, an effective multiple closely spaced extended target tracking approach is easily obtained. Simulation results verify the benefit of what we proposed—it has a much faster tracking speed without degrading the performance compared with other approaches, especially in a closely spaced target tracking scenario.

scholarly journals Robust Multisensor MeMBer Filter for Multiple Extended-Target Tracking

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xiaoke Lu ◽  
Zhiguo Zhang ◽  
Qing Li ◽  
Jinping Sun
Keyword(s):  
Target Tracking ◽  
Likelihood Function ◽  
Target State ◽  
Extended Target ◽  
Single Target ◽  
Simulation Results

This paper develops a robust extended-target multisensor multitarget multi-Bernoulli (ET-MS-MeMBer) filter for enhancing the unsatisfactory quality of measurement partitions arising in the classical ET-MS-MeMBer filter due to increased clutter intensities. Specifically, the proposed method considers the influence of the clutter measurement set by introducing the ratio of the target likelihood to the clutter likelihood. With the constraint of the clutter measurement set, it can obtain better multisensor measurement partitioning results under the original two-step greedy partitioning mechanism. Subsequently, the single-target multisensor likelihood function for the clutter case is derived. Simulation results reveal a favorable comparison to the ET-MS-MeMBer filter in terms of accuracy in estimating the target cardinality and target state under conditions with increased clutter intensities.

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