Auxiliary Particle Implementation of the Probability Hypothesis Density Filter

Whiteley, Nick; Singh, Sumeetpal S.; Godsill, Simon

doi:10.1109/ispa.2007.4383746

Cited by 16 publications

(11 citation statements)

References 29 publications

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“…In [15] the Gaussian mixture PHD filter is extended to linear Jump Markov multi-target models for tracking maneuvering targets. Recently, important developments such as the auxiliary particle-PHD filter [16] and measurement-oriented particle labeling technique [17], partially solve the clustering problem in the extraction of state estimates from the particle population. Clever uses of the PHD filter with measurement-driven birth intensity were independently proposed in [18] and [17] to improve tracking performance as well as obviating exact knowledge of the birth intensity.…”

Section: Introductionmentioning

confidence: 99%

CPHD Filtering With Unknown Clutter Rate and Detection Profile

Mahler

2011

IEEE Trans. Signal Process.

191

167

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In Bayesian multi-target filtering, we have to contend with two notable sources of uncertainty, clutter and detection. Knowledge of parameters such as clutter rate and detection profile are of critical importance in multi-target filters such as the probability hypothesis density (PHD) and cardinalized PHD (CPHD) filters. Significant mismatches in clutter and detection model parameters result in biased estimates. In practice, these model parameters are often manually tuned or estimated offline from training data. In this paper we propose PHD/CPHD filters that can accommodate model mismatch in clutter rate and detection profile. In particular we devise versions of the PHD/CPHD filters that can adaptively learn the clutter rate and detection profile while filtering. Moreover, closed-form solutions to these filtering recursions are derived using Beta and Gaussian mixtures. Simulations are presented to verify the proposed solutions.

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Section: Introductionmentioning

confidence: 99%

CPHD Filtering With Unknown Clutter Rate and Detection Profile

Mahler

2011

IEEE Trans. Signal Process.

191

167

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“…The conceptual framework for efficient particle PHD filter implementa tion has been cast in [4]: the proposal (importance) densities for drawing persistent (surviving) and newborn target particles need to dependent on the latest measurement set (which typically includes target-originated as well as false detections). How to construct these importance densities has been the topic of intensive research in the last decade [5], [6], [7], [8].…”

Section: Introductionmentioning

confidence: 99%

“…with B (z) = /i;k+1 (z) + (gk+1(zl·), I' k+1lk) + PD(g k+1(zl · ), Dk+1lk ,p) (5) Dk+1 Ik+1,p (x) and Dk+1lk+1 ,b (X) are intensity functions of per sistent and newborn objects, respectively.…”

Section: Introductionmentioning

confidence: 99%

Efficient update of persistent particles in the SMC-PHD filter

Ristić

2015

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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The paper is devoted to the implementation of the Sequential Monte Carlo Probability Hypothesis Density (SMC-PHD) filter. A mea surement driven proposal for persistent target particles requires the predicted persistent target particles to be partitioned in a probabilis tic manner using the received measurement set. Each partition is subsequently updated using a conveniently designed efficient pro posal distribution (in this paper we apply the progressive correc tion). The performance of the described algorithm is demonstrated in the context of autonomous tracking of multiple moving targets using bearings-only measurements.

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“…To alleviate this intractability, the probability hypothesis density (PHD) filter [7] and the cardinalised PHD (CPHD) filter [8] were proposed as the moment and cardinality approximations. Now, the existing closed-form solutions of PHD mainly include particle filter PHD [9,10], Gaussian mixture PHD (GM-PHD) filter [11] and their modified versions [12][13][14][15]. However, these algorithms only have a good performance in the multi-target tracking systems with known measurement noise variances; otherwise, their tracking performance may decrease greatly.…”

Section: Introductionmentioning

confidence: 99%

Adaptive probability hypothesis density filter based on variational Bayesian approximation for multi‐target tracking

Yang

2013

IET Radar, Sonar & Navigation

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Probability hypothesis density (PHD) filter has been demonstrated a promising algorithm for tracking an unknown number of targets in real time. However, this method can only be used in the multi-target tracking systems with known measurement noise variances; otherwise, the tracking performance will decline greatly. To solve this problem, an adaptive PHD filter algorithm is proposed based on the variational Bayesian approximation technique to recursively estimate the joint PHDs of the multi-target states and the time-varying measurement noise variances. First, the variational calculus method is employed to derive the multi-target estimate recursions, and then the Gaussian and the inverse Gamma distributions are introduced to approximate the joint posterior PHD, and achieve a closed-form solution. Simulation results show that the proposed algorithm can effectively estimate the unknown measurement noise variances and has a good performance of multitarget tracking with a strong robustness.

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Auxiliary Particle Implementation of the Probability Hypothesis Density Filter

Cited by 16 publications

References 29 publications

CPHD Filtering With Unknown Clutter Rate and Detection Profile

CPHD Filtering With Unknown Clutter Rate and Detection Profile

Efficient update of persistent particles in the SMC-PHD filter

Adaptive probability hypothesis density filter based on variational Bayesian approximation for multi‐target tracking

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