Robust CPHD algorithm for maneuvering targets tracking via airborne pulsed Doppler radar

Wang, Chaochen; Wu, Panlong; He, Shan; Peng, Yun

doi:10.1016/j.ijleo.2018.10.031

Cited by 8 publications

(4 citation statements)

References 19 publications

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“…process noise representing random acceleration in the x and y axis direction, w k ∼ Nð0, σ 2 w I 2 Þ, σ w = 10 m/s 2 . We assume that the target follows the constant acceleration (CA) model and the constant velocity (CV) model [27]. Their process equation F and process noise G are , ,…”

Section: Methodsmentioning

confidence: 99%

Learning to Track Multiple Radar Targets with Long Short-Term Memory Networks

Song

Cheng

et al. 2023

Wireless Communications and Mobile Computing

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Radar multitarget tracking in a dense clutter environment remains a complex problem to be solved. Most existing solutions still rely on complex motion models and prior distribution knowledge. In this paper, a new online tracking method based on a long short-term memory (LSTM) network is proposed. It combines state prediction, measurement association, and trajectory management functions in an end-to-end manner. We employ LSTM networks to model target motion and trajectory associations, relying on their strong learning ability to learn target motion properties and long-term dependence of trajectory associations from noisy data. Moreover, to address the problem of missing appearance information of radar targets, we propose an architecture based on the LSTM network to calculate similarity function by extracting long-term motion features. And the similarity is applied to trajectory associations to improve their robustness. Our proposed method is validated in simulation scenarios and achieves good results.

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Section: Methodsmentioning

confidence: 99%

Learning to Track Multiple Radar Targets with Long Short-Term Memory Networks

Song

Cheng

et al. 2023

Wireless Communications and Mobile Computing

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“…, R xz k , and R yz k can be directly referred to [8], whereas the µ Ω k , R xΩ k , R yΩ k , R zΩ k , and…”

Section: Mucm Modelmentioning

confidence: 99%

“…The second method is a converted measurement KF algorithm, whose the measurement is expressed in the pseudo linear form in the Cartesian coordinates by coordinate transformation; it mainly includes debiased converted measurement (DCM) and unbiased converted measurement (UCM). Approaches like these are easy to calculate and implement [7,8]. In particular, the UCM based on the multiplicative deviation compensation factor has better consistency and robustness than the DCM, whereas its disadvantage is that the error covariance matrix is calculated under the conditions of real range and one or two angles [9].…”

Section: Introductionmentioning

confidence: 99%

An EM algorithm for target tracking with an unknown correlation coefficient of measurement noise

He¹,

Wu²,

Peng³

et al. 2022

Meas. Sci. Technol.

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In this paper, an expectation maximization based sequential modified unbiased converted measurement Kalman filter is proposed for target tracking with an unknown correlation coefficient of measurement noise between the range and the range rate. Firstly, a pseudo measurement is constructed by multiplying the range and the range rate to reduce the strong nonlinearity between the measurement and the target state. The mean and covariance of converted errors are subsequentlsubsequently derived by modified unbiased converted measurement to weaken the error caused by the linearization of the measurement equation, which is effectively to improve the dynamic accuracy of target tracking. Then, the converted errors of the position and the pseudo measurement are decorrelated by the Cholesky factorization and thus to obtain the posterior probability distribution of the state by using the sequential filtering in the Bayesian framework. Finally, the expectation maximization is introduced in the updating procedure of the pseudo measurement to jointly estimate the target state and the correlation coefficient. The target tracking scenario with an unknown correlation coefficient is built to demonstrate the validness and feasibility of the proposed algorithm. Simultaneously, the results of the normalized error squared validate the consistency of the modified unbiased converted measurement.

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“…In [15], based on the famous Faà di Bruno determinant, a tractable recursion computation technique of the general cardinality prediction equation is presented. In [16], a robust CPHD based on interacting multiple model (RCPHD-IMM) is proposed for multiple maneuvering targets tracking under the Doppler blind zone of airborne pulse Doppler radar. In [17], to accommodate unknown target detection probability and nonnegative non-Gaussian parameters, a new implementation based on inverse gamma Gaussian mixtures is proposed, which introduces a location independent feature and gamma functions to determine detection probability incorporated into the recursions.…”

Section: Introductionmentioning

confidence: 99%

Bearings-only multitarget tracking based onRao-Blackwellized particle CPHD filter

Zhang

2021

International Journal of Circuits, Systems and Signal Processing

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Following Mahler’s framework forinformation fusion, this paper develops a implementationof cardinalized probability hypothesis density (CPHD)filter for bearings-only multitarget tracking.Rao-Blackwellized method is introduced in the CPHDfiltering framework for mixed linear/nonlinear state spacemodels. The sequential Monte Carlo (SMC) method is usedto predict and estimate the nonlinear state of targets.Kalman filter (KF) is adopted to estimate the linear stateswith the information embedded in the estimated nonlinearstates. The multitarget state estimates are extracted byutilizing the kernel density estimation (KDE) theory andmean-shift algorithm to enhance tracking performance.Moreover, the computational load of the filter is analyzedby introducing equivalent flop measure. Finally, theperformance of the proposed Rao-Blackwellized particleCPHD filter is evaluated through a challengingbearings-only multitarget tracking simulation experiment.

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Robust CPHD algorithm for maneuvering targets tracking via airborne pulsed Doppler radar

Cited by 8 publications

References 19 publications

Learning to Track Multiple Radar Targets with Long Short-Term Memory Networks

Learning to Track Multiple Radar Targets with Long Short-Term Memory Networks

An EM algorithm for target tracking with an unknown correlation coefficient of measurement noise

Bearings-only multitarget tracking based onRao-Blackwellized particle CPHD filter

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