Particle filter tracking for long range radars

Romeo, Kevin; Willett, Peter; Bar‐Shalom, Yaakov

doi:10.1117/12.924336

Cited by 3 publications

(3 citation statements)

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“…The most advanced methods for monostatic tracking using r-u-v measurements are a number of particle filter techniques [243] and Gaussian mixture trackers [178,278,293], which split the target's state into a Gaussian mixture (or merge components of the mixture) based on a divergence measure derived in [292] relative to the curvature of the measurement. The bestperforming particle filter techniques in [243] are a form of the regularized particle filter and a Gaussian mixture sigma-point particle filter (GMSPF).…”

Section: A Estimation Using Nonlinear Measurementsmentioning

confidence: 99%

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Basic tracking using nonlinear 3D monostatic and bistatic measurements

Crouse¹

2014

IEEE Aerosp. Electron. Syst. Mag.

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Monostatic and bistatic position and Doppler measurements used in radar and sonar systems are nonlinear transformations of a Cartesian state. These nonlinearities pose a challenge for many target tracking algorithms, causing the so-called contact lens problem, which describes the nonlinear appearance of the measurement probability density function in Cartesian coordinates. This tutorial considers methods for measurement filtering (tracking without considering data association) using a single-Gaussian approximation when monostatic and bistatic position and Doppler measurements are available. The connection between the cubature Kalman filter and numerous other filtering algorithms is shown, and the accuracy and consistency of different algorithms are compared through simulation. An effort is made to express the geometric relationships associated with multistatic tracking in a simple vectorial manner. This tutorial focuses on basic tracking, and the companion tutorials "Tracking Using 3D Monostatic and Bistatic Measurements in Refractive Environments" and "Basic Tracking Using Nonlinear Continuous-Time Dynamic Models" extend the results to more sophisticated physical models.

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Section: A Estimation Using Nonlinear Measurementsmentioning

confidence: 99%

“…The bestperforming particle filter techniques in [243] are a form of the regularized particle filter and a Gaussian mixture sigma-point particle filter (GMSPF). The GMSPF was presented in [299] as a generalization of a sigma-point filter.…”

Section: A Estimation Using Nonlinear Measurementsmentioning

confidence: 99%

Basic tracking using nonlinear 3D monostatic and bistatic measurements

Crouse¹

2014

IEEE Aerosp. Electron. Syst. Mag.

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“…Many particle filter versions exist that attempt to alleviate these problems, such as the auxiliary particle filter [1], the regularized particle filter (RPF) [1,15], the resample-move particle filter [7], and the Gaussian mixture sigma-point particle filter (GMSPPF) [24]. Some of these versions were examined in [17]. We also evaluated a particle filter with proposal density linked to the EKF [23], but it was unable to run in the scenarios explored in this paper because of the aforementioned problems (degeneracy and impoverishment) that occur with very low process noise.…”

Section: Introductionmentioning

confidence: 99%

Particle filter tracking for banana and contact lens problems

Romeo

Willett

Bar‐Shalom

2015

IEEE Trans. Aerosp. Electron. Syst.

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In this paper we present an approach for tracking with high-bandwidth radar in long-range scenarios. We consider both two-and three-dimensional measurements in polar and r-u-v, respectively. We show that in these scenarios the extended Kalman filter is not desirable, because it suffers from major consistency problems, and most types of particle filters suffer from a loss of diversity among particles after resampling. This leads to sample impoverishment and divergence of the filter. In the scenarios studied, this loss of diversity can be attributed to the very low process noise. However, a regularized particle filter is shown to avoid this diversity problem while producing consistent results. The regularization is accomplished using a generalized version of the Epanechnikov kernel.

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Basic tracking using nonlinear continuous-time dynamic models [Tutorial]

Crouse

2015

IEEE Aerosp. Electron. Syst. Mag.

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Particle filter tracking for long range radars

Cited by 3 publications

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Basic tracking using nonlinear 3D monostatic and bistatic measurements

Basic tracking using nonlinear 3D monostatic and bistatic measurements

Particle filter tracking for banana and contact lens problems

Basic tracking using nonlinear continuous-time dynamic models [Tutorial]

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