Iterative RANSAC based adaptive birth intensity estimation in GM-PHD filter for multi-target tracking

Wu, Jingjing; Li, Ké; Zhang, Qiuju; An, Wei; Jiang, Yi; Xueliang, Ping; Chen, Peng

doi:10.1016/j.sigpro.2016.09.001

Cited by 25 publications

(16 citation statements)

References 27 publications

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“…To cope with strong clutter situation, [20] presents a dual threshold particle PHD filter with unknown target birth intensity. Different from above solutions based on direct measurement-driven estimation, [21] and [22] give another way to deal with the target birth intensity. Before the PHD update operator, both introduce extra steps to distinguish the newborn targets originated measurements and other measurements.…”

Section: Introductionmentioning

confidence: 99%

“…The second step is based on the coverage rate. In [22], the birth intensity is estimated based on iterative random sample consensus (RANSAC) algorithm within a sliding window.…”

Section: Introductionmentioning

confidence: 99%

“…All the above PHD filters with birth intensity estimation only operate on the current or the current and previous consecutive scan data for multi-target tracking, except for [22]. But in the iterative RANSAC based filter, in essence, the line model is constructed by using the measurements of the current scan and the previous scan.…”

Section: Introductionmentioning

confidence: 99%

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The Modified Probability Hypothesis Density Filter With Adaptive Birth Intensity Estimation for Multi-Target Tracking in Low Detection Probability

Zhu

Li²,

Pan

et al. 2020

IEEE Access

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The existing Probability Hypothesis Density (PHD) filters with birth intensity estimation only operate on single or two consecutive scan data for multi-target tracking. However, for those targets with low detection probability, it is hard to achieve a satisfactory level of track initiation and maintenance. To overcome the weakness above, we propose a modified PHD filter with adaptive birth intensity estimation. The core of the proposed filter is to define two state sets as the formal set and the temporary set. In the framework of measurement driven estimation, we classify the measurements into three categories depending on whether it is in the neighborhood of the state in above two sets. And the birth states of the formal set and the temporary set are generated by the classified measurements respectively. In addition, if there is no matching measurement for the state in the formal set, duplicate the corresponding state as the birth state of the temporary set. For each state in temporary set, we introduce a forgetting factor and a dynamic detection probability in filter to cope with the rapid decrease of its intensity due to the absence of measurement. If its forgetting factor is over the dead threshold, the state will be deleted from the set. Based on the principles above, we derive the Gaussian-mixture (GM) implementation of the PHD filter proposed in this paper. Experiment results show that, in low detection probability scenario, the modified PHD filter outperforms other PHD filters with birth intensity estimation. INDEX TERMS Probability hypothesis density filter, multi-target tracking, birth intensity estimation, low detection probability.

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Section: Introductionmentioning

confidence: 99%

“…The second step is based on the coverage rate. In [22], the birth intensity is estimated based on iterative random sample consensus (RANSAC) algorithm within a sliding window.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Modified Probability Hypothesis Density Filter With Adaptive Birth Intensity Estimation for Multi-Target Tracking in Low Detection Probability

Zhu

Li²,

Pan

et al. 2020

IEEE Access

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“…Target Tracking is an essential technology in both military and civil fields, and it is critical to the success of many tasks, such as target recognition and attack [1][2][3][4][5]. The main mission of target tracking is to show the value of current state estimation and latter state prediction by using sensor measurements.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Unscented Kalman Filter for Target Tracking in the Presence of Nonlinear Systems Involving Model Mismatches

et al. 2017

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Abstract:In order to improve filtering precision and restrain divergence caused by sensor faults or model mismatches for target tracking, a new adaptive unscented Kalman filter (N-AUKF) algorithm is proposed. First of all, the unscented Kalman filter (UKF) problem to be solved for systems involving model mismatches is described, after that, the necessary and sufficient condition with third order accuracy of the standard UKF is given and proven by using the matrix theory. In the filtering process of N-AUKF, an adaptive matrix gene is introduced to the standard UKF to adjust the covariance matrixes of the state vector and innovation vector in real time, which makes full use of normal innovations. Then, a covariance matching criterion is designed to judge the filtering divergence. On this basis, an adaptive weighted coefficient is applied to restrain the divergence. Compared with the standard UKF and existing adaptive UKF, the proposed UKF algorithm improves the filtering accuracy, rapidity and numerical stability remarkably, moreover, it has a good adaptive capability to deal with sensor faults or model mismatches. The performance and effectiveness of the proposed UKF is verified in a target tracking mission.

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“…Performing accurate maneuvering target tracking and precise locating are the core issues in the field of space target surveillance [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

A Cubature-Principle-Assisted IMM-Adaptive UKF Algorithm for Maneuvering Target Tracking Caused by Sensor Faults

et al. 2017

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Featured Application: The work is very effective and applicable in the fields of the maneuvering target tracking and navigations system design.Abstract: Aimed at solving the problem of decreased filtering precision while maneuvering target tracking caused by non-Gaussian distribution and sensor faults, we developed an efficient interacting multiple model-unscented Kalman filter (IMM-UKF) algorithm. By dividing the IMM-UKF into two links, the algorithm introduces the cubature principle to approximate the probability density of the random variable, after the interaction, by considering the external link of IMM-UKF, which constitutes the cubature-principle-assisted IMM method (CPIMM) for solving the non-Gaussian problem, and leads to an adaptive matrix to balance the contribution of the state. The algorithm provides filtering solutions by considering the internal link of IMM-UKF, which is called a new adaptive UKF algorithm (NAUKF) to address sensor faults. The proposed CPIMM-NAUKF is evaluated in a numerical simulation and two practical experiments including one navigation experiment and one maneuvering target tracking experiment. The simulation and experiment results show that the proposed CPIMM-NAUKF has greater filtering precision and faster convergence than the existing IMM-UKF. The proposed algorithm achieves a very good tracking performance, and will be effective and applicable in the field of maneuvering target tracking.

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Iterative RANSAC based adaptive birth intensity estimation in GM-PHD filter for multi-target tracking

Cited by 25 publications

References 27 publications

The Modified Probability Hypothesis Density Filter With Adaptive Birth Intensity Estimation for Multi-Target Tracking in Low Detection Probability

The Modified Probability Hypothesis Density Filter With Adaptive Birth Intensity Estimation for Multi-Target Tracking in Low Detection Probability

Adaptive Unscented Kalman Filter for Target Tracking in the Presence of Nonlinear Systems Involving Model Mismatches

A Cubature-Principle-Assisted IMM-Adaptive UKF Algorithm for Maneuvering Target Tracking Caused by Sensor Faults

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