Integrated real-time estimation of clutter density for tracking

Li, X.R.; Li, Ning

doi:10.1109/78.869029

Cited by 61 publications

(17 citation statements)

References 4 publications

(6 reference statements)

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“…Then, it becomes possible to recursively update ∂ θ D k|k (x) through equations similar to (1) and (2). There is…”

Section: Methodology Of ML Estimator For Clutter Parametermentioning

confidence: 99%

“…f k+1|k (x|x ) is the Markov transition density for single target; p S (x ) is the surviving probability for already existing target whose state is x at time step k; b k+1|k (x) is the PHD for completely new targets who appear at time step k + 1 with state x; L z (x) is abbreviation of f k+1 (z|x), the sensor likelihood function; p D (x) is abbreviation of p D,k+1 (x), the target detection probability at time step k + 1; λ k+1 (z k+1 ) represents the intensity function of non-homogeneous Poisson clutter points. To avoid the calculation of multiple integrals in (1) and (2), SMC implementation of PHD filter has been presented. 9 There, target PHD has been approximated by a large number of weighted particles and a modified importance sampling-resampling method has been proposed.…”

Section: Methodology Of ML Estimator For Clutter Parametermentioning

confidence: 99%

“…2 However, there it has still assumed that clutter points are uniformly distributed in validation gate.…”

Section: Introductionmentioning

confidence: 99%

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Integrated clutter estimation and target tracking using Poisson point process

Chen

Kirubarajan

Pelletier³

2009

SPIE Proceedings

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In this paper, methods of tracking multiple targets in non-homogeneous clutter background is studied. In many scenarios, after detection process, measurement points provided by the sensor (e.g., sonar, infrared sensor, radar) are not distributed uniformly in the surveillance region. On the other hand, in order to obtain accurate results, the target tracking filter requires information about clutter's spatial density. Thus, non-homogeneous clutter point spatial density has to be estimated based on the measurement point set and tracking filter's outputs. Also, due to the requirement of compatibility, it is desirable for this estimation method to be integrated into current tracking filters. In this paper, a recursive maximum likelihood method and an approximated Bayesian method are proposed to estimate the clutter point spatial density in non-homogeneous clutter background and both will in turn be integrated into Probability Hypothesis Density (PHD) filter. Here, non-homogeneous Poisson point processes, whose intensity function are assumed to be mixtures of Gaussian functions, are used to model clutter points. The mean and covariance of each Gaussian function is estimated and used in the update equation of the PHD filter. Simulation results show that the proposed methods are able to estimate the clutter point spatial density and improve the performance of PHD filter over non-homogeneous clutter background.

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“…Then, it becomes possible to recursively update ∂ θ D k|k (x) through equations similar to (1) and (2). There is…”

Section: Methodology Of ML Estimator For Clutter Parametermentioning

confidence: 99%

Section: Methodology Of ML Estimator For Clutter Parametermentioning

confidence: 99%

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Integrated clutter estimation and target tracking using Poisson point process

Chen

Kirubarajan

Pelletier³

2009

SPIE Proceedings

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“…The multiple-target, distributed network and finite resolution versions of the IPDAF are also available [17,7,6]. The IPDAF also has built-in clutter density estimators [15] that are superior to the standard parametric and non-parametric versions of the PDAF.…”

Section: Introductionmentioning

confidence: 99%

<title>2M-PDAF: an integrated two-model probabilistic data association filter</title>

1999

SPIE Proceedings

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The well-known probabilistic data association (PDA) filter handles the uncertainty in measurement origins inherent in tracking-in-cluuer problems by using a probabilistically weighted sum of all measurements in the gate. In fact, the measurements in the gate may or may not include the one originated from the target. As such, two hypothetical models can be set up, corresponding to the events that the target measurement is and is not in the gate, respectively. This paper presents an approach that integrates the PDA filter with the multiple-model method in a coherent manner based on the use of the above two hypothetical models. It is shown theoretically that the standard PDA filter is a special case of the first-order Generalized Pseudo Bayesian algorithm (GPB1) in the proposed formulation using a particular set of model transition probabilities. It is then proposed to adopt the superior interacting multiple-model (1MM) architecture in this new formulation to improve the performance. The new algorithm is capable of achieving better performance by tuning the transition probabilities at a computational complexity comparable to that of the PDA filter. Simulation results are provided.

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“…The drawback of JPDA and JIPDA is that their computational complexity increases exponentially as the target density increases, making them unsuitable in many situations. Some other independent techniques for on-line clutter rate estimation have also been proposed in [8] and [9], and comparison of these with the bootstrap GMCPHD may be the subject of future work.…”

mentioning

confidence: 98%

Multitarget Filtering With Unknown Clutter Density Using a Bootstrap GMCPHD Filter

Beard

2013

IEEE Signal Process. Lett.

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It was recently demonstrated that the Gaussian Mixture Cardinalised Probability Hypothesis Density (GMCPHD) filter can be used when the clutter density is unknown. Here we examine the performance of this filter, and as one would expect, it does not do as well as the conventional GMCPHD with matched clutter density. To improve the performance, we propose a bootstrap filtering scheme, and demonstrate by simulations on a bearings-only multitarget filtering scenario, that it is capable of performing almost as well as the matched GMCPHD filter.

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Integrated real-time estimation of clutter density for tracking

Cited by 61 publications

References 4 publications

Integrated clutter estimation and target tracking using Poisson point process

Integrated clutter estimation and target tracking using Poisson point process

<title>2M-PDAF: an integrated two-model probabilistic data association filter</title>

Multitarget Filtering With Unknown Clutter Density Using a Bootstrap GMCPHD Filter

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