Dual Detection-Guided Newborn Target Intensity Based on Probability Hypothesis Density for Multiple Target Tracking

Gao, Li; Ma, Yongjie

doi:10.3837/tiis.2016.10.025

Cited by 2 publications

(4 citation statements)

References 22 publications

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“…As we can see (13) that Poisson and Bernoulli part can be predicted separately. Given the posterior PMBM density at k-1th scanning period parameterized by Poisson intensity 1 ( )…”

Section: Prediction Processmentioning

confidence: 71%

“…According to whether the predicted distribution and posterior distribution have the same form as the initial prior, the filter on RFS can be divided into the conjugate RFS filter and the nonconjugate RFS filter [10][11]. The probability hypothesis density (PHD) filter [12][13], the cardinalized probability hypothesis density (CPHD) filter [14], the multiple target multi-Bernoulli (MeMBer) filter [15] belong to non-conjugate RFS filter, i.e., to approximate the posterior multi-target density at low cost. The conjugate RFS filter possesses higher filtering accuracy and easier computation than the non-conjugate one due to the conjugacy property [16] and it can be divided into two types.…”

Section: Introductionmentioning

confidence: 99%

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Time-Matching Poisson Multi-Bernoulli Mixture Filter For Multi-Target Tracking In Sensor Scanning Mode

2023

KSII TIIS

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In Bayesian multi-target tracking, the Poisson multi-Bernoulli mixture (PMBM) filter is a state-of-the-art filter based on the methodology of random finite set which is a conjugate prior composed of Poisson point process (PPP) and multi-Bernoulli mixture (MBM). In order to improve the random finite set-based filter utilized in multi-target tracking of sensor scanning, this paper introduces the Poisson multi-Bernoulli mixture filter into time-matching Bayesian filtering framework and derive a tractable and principled method, namely: the time-matching Poisson multi-Bernoulli mixture (TM-PMBM) filter. We also provide the Gaussian mixture implementation of the TM-PMBM filter for linear-Gaussian dynamic and measurement models. Subsequently, we compare the performance of the TM-PMBM filter with other RFS filters based on time-matching method with different birth models under directional continuous scanning and out-of-order discontinuous scanning. The results of simulation demonstrate that the proposed filter not only can effectively reduce the influence of sampling time diversity, but also improve the estimated accuracy of target state along with cardinality.

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“…As we can see (13) that Poisson and Bernoulli part can be predicted separately. Given the posterior PMBM density at k-1th scanning period parameterized by Poisson intensity 1 ( )…”

Section: Prediction Processmentioning

confidence: 71%

Section: Introductionmentioning

confidence: 99%

Time-Matching Poisson Multi-Bernoulli Mixture Filter For Multi-Target Tracking In Sensor Scanning Mode

2023

KSII TIIS

View full text Add to dashboard Cite

show abstract

“…Target tracking typically assumes that each target can produce at most one measurement each scan. However, in a modern high-precision sensor system, a target may produce more than a single measurement per scan, such a target called an extended target, and the typical multiple target tracking (MTT) approaches [1][2][3] fail to track the extended targets accuratly. Therefore, a growing number of works have been done for extended target tracking (ETT) [4][5][6][7][8][9] in resent years, and this is espacially true for multiple extended target tracking (METT) [10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

“…An important implementation of a MTT PHD filter is the Gaussian mixture PHD (GM-PHD) filter [2], which employs the Gaussian mixture method to approximate the target PHD. The discussion of the newborn target intensity of a GM-PHD can be found in [3].…”

Section: Introductionmentioning

confidence: 99%

Novel Partitioning Algorithm for a Gaussian Inverse Wishart PHD Filter for Extended Target Tracking

2017

KSII TIIS

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Use of the Gaussian inverse Wishart PHD (GIW-PHD) filter has demonstrated promise as an approach to track an unknown number of extended targets. However, the partitioning approaches used in the GIW-PHD filter, such as distance partition with sub-partition (DP-SP), prediction partition (PP) and expectation maximization partition (EMP), fails to provided accurate partition results when targets are spaced closely together and performing maneuvers. In order to improve the performance of a GIW-PHD filter, this paper presents a cooperation partitioning (CP) algorithm to solve the partitioning issue when targets are spaced closely together. In the GIW-PHD filter, the DP-SP is insensitive to target maneuvers but sensitive to the differences in target sizes, while EMP is the opposite. The proposed CP algorithm is a fusion approach of DP-SP and EMP, which employs EMP as a sub-partition approach after DP. Therefore, the CP algorithm will be sensitive to neither target maneuvers nor differences in target sizes. The simulation results show that the use of the proposed CP algorithm will improve the performance of the GIW-PHD filter when targets are spaced closely together.

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Dual Detection-Guided Newborn Target Intensity Based on Probability Hypothesis Density for Multiple Target Tracking

Cited by 2 publications

References 22 publications

Time-Matching Poisson Multi-Bernoulli Mixture Filter For Multi-Target Tracking In Sensor Scanning Mode

Time-Matching Poisson Multi-Bernoulli Mixture Filter For Multi-Target Tracking In Sensor Scanning Mode

Novel Partitioning Algorithm for a Gaussian Inverse Wishart PHD Filter for Extended Target Tracking

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