Two Measurement Set Partitioning Algorithms for the Extended Target Probability Hypothesis Density Filter

Han, Yulan; Han, Chongzhao

doi:10.3390/s19122665

Cited by 5 publications

(8 citation statements)

References 31 publications

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“…e motion state equation of the target is as shown in equation ( 1) above, where the extended target has a system evolution equation when it moves in a uniform straight line as equation (12), where…”

Section: Constant Motionmentioning

confidence: 99%

“…e multitarget tracking algorithm builds a random nite set based on multitarget states and measurements separately [9][10][11], which avoids data correlation but requires consideration of the division of the measurement set at each moment [12][13][14]. In addition, this algorithm only provides the state set at each moment, and a reasonable track generation algorithm needs to be added [15].…”

Section: Introductionmentioning

confidence: 99%

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An Extended Target Tracking Algorithm Based on Gaussian Surface Fitting

Han

Gang-dun

et al. 2022

Mathematical Problems in Engineering

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Although the existing algorithms have done a lot of progress in extended target tracking (ETT), most algorithms are limited by the assumption of the target shape, and there are problems with parameter calculations. To solve the problems above, an extended target tracking algorithm based on level set and Gaussian Surface fitting (GSF), is proposed. Different from the existing extended target tracking algorithms, the proposed algorithm uses a level set to implicitly describe the evolution of the estimated shape, eliminates the parameterization process, and uses the GSF to construct the measurement source spatial distribution function (MSSDF) of the extended target. The proposed algorithm can obtain the estimated shape of the extended target without knowing the prior shape and size of the target. The simulation results verify the effectiveness and reliability of the proposed algorithm.

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Section: Constant Motionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Extended Target Tracking Algorithm Based on Gaussian Surface Fitting

Han

Gang-dun

et al. 2022

Mathematical Problems in Engineering

Self Cite

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“…In this case, a target should be regarded as extended if its extension is much larger than the sensor resolution. Recently, multiple extended target tracking [11][12][13][14][15][16] has been attracting attention. However, it is still a complicated problem because there are unknown and uncertain associations between targets and measurements.…”

Section: Introductionmentioning

confidence: 99%

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

Sun

et al. 2020

Applied Sciences

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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.

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“…Faced with unknown backgrounds, such as unknown detection probability, unknown clutter parameter, several improved PHD filters can estimate background parameters while tracking [36,37]. In non-standard target observation model, several improved PHD filters were proposed to track extended target [38,39].…”

Section: Introductionmentioning

confidence: 99%

Refined PHD Filter for Multi-Target Tracking under Low Detection Probability

Wang

Bao

Chen

2019

Sensors

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Radar target detection probability will decrease as the target echo signal-to-noise ratio (SNR) decreases, which has an adverse influence on the result of multi-target tracking. The performances of standard multi-target tracking algorithms degrade significantly under low detection probability in practice, especially when continuous miss detection occurs. Based on sequential Monte Carlo implementation of Probability Hypothesis Density (PHD) filter, this paper proposes a heuristic method called the Refined PHD (R-PHD) filter to improve multi-target tracking performance under low detection probability. In detail, this paper defines a survival probability which is dependent on target state, and labels individual extracted targets and corresponding particles. When miss detection occurs due to low detection probability, posterior particle weights will be revised according to the prediction step. Finally, we transform the target confirmation problem into a hypothesis test problem, and utilize sequential probability ratio test to distinguish real targets and false alarms in real time. Computer simulations with respect to different detection probabilities, average numbers of false alarms and continuous miss detection durations are provided to corroborate the superiority of the proposed method, compared with standard PHD filter, Cardinalized PHD (CPHD) filter and Cardinality Balanced Multi-target Multi-Bernoulli (CBMeMBer) filter.

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Two Measurement Set Partitioning Algorithms for the Extended Target Probability Hypothesis Density Filter

Cited by 5 publications

References 31 publications

An Extended Target Tracking Algorithm Based on Gaussian Surface Fitting

An Extended Target Tracking Algorithm Based on Gaussian Surface Fitting

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

Refined PHD Filter for Multi-Target Tracking under Low Detection Probability

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