Cost-Reference Particle Filter for Cognitive Radar Tracking Systems with Unknown Statistics

Zhong, Lei; Li, Yong; Cheng, Wei; Zheng, Yi

doi:10.3390/s20133669

Cited by 4 publications

(2 citation statements)

References 46 publications

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“…In view of the fact that the statistical characteristics of the noise are unknown, the cost-reference particle filter (CRPF) was proposed for the first time [ 9 ]. This method does not need to know the statistical characteristics of process noise and measurement noise [ 10 ]. It calculates the weight of each particle by a self-defined cost function and risk function, and then resamples according to the weight.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Cost-Reference Particle Filter with Resampling of Multi-Population Cooperation

Zhang

Ren

Duan

et al. 2023

Sensors

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Although the cost-reference particle filter (CRPF) has a good advantage in solving the state estimation problem with unknown noise statistical characteristics, its estimation accuracy is still affected by the lack of particle diversity and sensitivity to the particles’ initial value. In order to solve these problems of the CRPF, this paper proposed an intelligent cost-reference particle filter algorithm based on multi-population cooperation. A multi-population cooperative resampling strategy based on ring structure was designed. The particles were divided into multiple independent populations upon initialization, and each population generated particles with a different initial distribution. The particles in each population were divided into three different particle sets with high, medium and low weights by the golden section ratio according to the weight. The particle sets with high and medium weights were retained. Then, a cooperative strategy based on Gaussian mutation was designed to resample the low-weight particle set of each population. The high-weight particles of the previous population in the ring structure were randomly selected for Gaussian mutation to replace the low-weight particles in the current population. The low-weight particles of all populations were resampled in turn. The simulation results show that the intelligent CRPF based on multi-population cooperation proposed in this paper can reduce the sensitivity of the CRPF to the particles’ initial value and improve the particle diversity in resampling. Compared with the general CRPF and intelligent CRPF with adaptive MH resampling (MH-CRPF), the RMSE and MAE of the proposed method are lower.

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Section: Introductionmentioning

confidence: 99%

An Intelligent Cost-Reference Particle Filter with Resampling of Multi-Population Cooperation

Zhang

Ren

Duan

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Few works are taken the particle filter (PF) into account. A parallel structure of extended Kalman filter (EKF) and PF has been adopted to deal with the nonlinear state estimation, but it is still a standard KF in essence if the EKF is used in waveform selection [11]. It is only applicable for the estimation of weak nonlinear state because the state distribution is approximated by a Gaussian random variable (GRV), which is then propagated analytically through the first-order linearization of the nonlinear system.…”

Section: Introductionmentioning

confidence: 99%

Robust Cognitive Radar Tracking Based on Adaptive Unscented Kalman Filter in Uncertain Environments

Zhong

Cheng

et al. 2020

IEEE Access

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This paper presents an approach for cognitive radar to track the target state despite unknown statistics or sudden changes in noise exposed to uncertain environments. Given the prior knowledge or accurate estimation of the noise, cognitive radar can adaptively adjust the waveform in the transmitter by perceiving the environment. However, the measurement noise usually contains a priori unknown parameters which are difficult to be estimated, or the process noise without empirical model might be improperly configured. Besides, they may change abruptly or time-varying in practice. These issues are prone to occur in cognitive radar, leading to performance decline or failure in tracking ultimately. To alleviate this problem, a robust cognitive radar tracking method based on adaptive unscented Kalman filter (AUKF) is proposed in this paper. Specifically, UKF is used to derive the cognitive mathematical model and estimate the states in nonlinear systems. Moreover, a robust adaptive mechanism is designed in the cognitive framework, which is independent of prior information. When the mismatch between the noise in the model and the noise in the environment emerges, the noise covariance can be corrected adaptively. To verify the efficiency of the scheme, maneuvering target tracking experiments are carried out in three uncertain noise scenarios. Simulation results show that the scheme outperforms the existing adaptive UKF and cognitive radar algorithms in terms of intelligence and robustness. INDEX TERMS cognitive radar, adaptive filtering, robust estimation, target tracking, uncertain noise.

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Sign Language Detection Using AI Machine Learning

Antad,

Khot,

Khot

et al. 2024

Lecture Notes in Electrical Engineering

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Cost-Reference Particle Filter for Cognitive Radar Tracking Systems with Unknown Statistics

Cited by 4 publications

References 46 publications

An Intelligent Cost-Reference Particle Filter with Resampling of Multi-Population Cooperation

An Intelligent Cost-Reference Particle Filter with Resampling of Multi-Population Cooperation

Robust Cognitive Radar Tracking Based on Adaptive Unscented Kalman Filter in Uncertain Environments

Sign Language Detection Using AI Machine Learning

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