PHD and CPHD Filtering With Unknown Detection Probability

Li, Chen-ming; Wang, Wenguang; Kirubarajan, T.; Sun, Jinping; Peng, Lei

doi:10.1109/tsp.2018.2835398

Cited by 59 publications

(47 citation statements)

References 28 publications

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“…In addition, the stronger the SNR and echo amplitude the higher the target detection probability. Therefore, the IGGM model and its analytical form of CPHD/PHD filters were proposed in [29]. In the IGGM model, it is assumed that the single-target state x contains the kinematic state x and also the feature a used for detection, i.e., x = [ x, a] T .…”

Section: The Iggm Modelmentioning

confidence: 99%

“…In Equation (63), we set F th to 5.5, δ 1 to 4, and δ 2 to 2. We assumed that the clutter measurements generated by the sensors were Poisson with the uniform spatial distribution c( z) and mean clutter rate λ c , i.e., κ For efficiency purposes, the pruning and merging methods in [10,29] were adopted in this paper. For the greedy measurement partitioning algorithm, the maximum number of the measurement subsets W max was set to 4, and the maximum number of partitioning hypotheses P max was set to 4.…”

Section: System Modelmentioning

confidence: 99%

“…However, the filtering performance deteriorates when the detection probability is low, and poor model initialization parameters can adversely affect the filtering performance. For this reason, the inverse gamma Gaussian mixture (IGGM) model is used in CPHD/PHD filters [29]. Specifically, the feature used for detection is an inverse gamma distribution, and by combining the feature into the target motion state, the performance loss caused by the mismatch of the detection probability parameters is reduced.…”

Section: Introductionmentioning

confidence: 99%

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Multisensor RFS Filters for Unknown and Changing Detection Probability

Zhang

Sun

2019

Electronics

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The detection probability is an important parameter in multisensor multitarget tracking. The existing multisensor multi-Bernoulli (MS-MeMBer) filter and multisensor cardinalized probability hypothesis density (MS-CPHD) filter require that detection probability is a priori. However, in reality, the value of the detection probability is constantly changing due to the influence of sensors, targets, and other environmental characteristics. Therefore, to alleviate the performance deterioration caused by the mismatch of the detection probability, this paper applies the inverse gamma Gaussian mixture (IGGM) distribution to both the MS-MeMBer filter and the MS-CPHD filter. Specifically, the feature used for detection is assumed to obey the inverse gamma distribution and is statistically independent of the target’s spatial position. The feature is then integrated into the target state to iteratively estimate the target detection probability as well as the motion state. The experimental results demonstrate that the proposed methods can achieve a better filtering performance in scenarios with unknown and changing detection probability. It is also shown that the distribution of the sensors has a vital influence on the filtering accuracy, and the filters perform better when sensors are dispersed in the monitoring area.

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Section: The Iggm Modelmentioning

confidence: 99%

Section: System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multisensor RFS Filters for Unknown and Changing Detection Probability

Zhang

Sun

2019

Electronics

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“…The PHD filter is an approximation of the Bayesian estimator described by Equations (17) and (18) [31]. Rather than propagating the FISST PDF f k|k (X k |Z 1:k , θ), we can only propagate PHD D k|k (x|Z 1:k , θ), defined by Equation (3).…”

Section: Phd Based Bayesian Equationsmentioning

confidence: 99%

“…Update the correction factor β s = min{1 − Λ s−1 , −φ/ψ 31 Compute the maximum a posterior (MAP) estimate θ from the sample {θ S j } M j=1 .…”

Section: Simulated Tempering Based Importance Samplingmentioning

confidence: 99%

Random Finite Set Based Parameter Estimation Algorithm for Identifying Stochastic Systems

Wang

Peng

et al. 2018

Entropy

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Parameter estimation is one of the key technologies for system identification. The Bayesian parameter estimation algorithms are very important for identifying stochastic systems. In this paper, a random finite set based algorithm is proposed to overcome the disadvantages of the existing Bayesian parameter estimation algorithms. It can estimate the unknown parameters of the stochastic system which consists of a varying number of constituent elements by using the measurements disturbed by false detections, missed detections and noises. The models used for parameter estimation are constructed by using random finite set. Based on the proposed system model and measurement model, the key principles and formula derivation of the proposed algorithm are detailed. Then, the implementation of the algorithm is presented by using sequential Monte Carlo based Probability Hypothesis Density (PHD) filter and simulated tempering based importance sampling. Finally, the experiments of systematic errors estimation of multiple sensors are provided to prove the main advantages of the proposed algorithm. The sensitivity analysis is carried out to further study the mechanism of the algorithm. The experimental results verify the superiority of the proposed algorithm.

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