The Sigma Point Class: The Unscented Kalman Filter

Haug, Anton J.

doi:10.1002/9781118287798.ch9

Bayesian Estimation and Tracking 2012

DOI: 10.1002/9781118287798.ch9

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The Sigma Point Class: The Unscented Kalman Filter

Anton J. Haug¹

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2020

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

Zhong

Cheng

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

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The Sigma Point Class: The Unscented Kalman Filter

Cited by 1 publication

References 16 publications

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

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

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