Passive Target Positioning Algorithm for Mixed Noise

doi:10.3969/j.issn.1000-1093.2021.09.013

Abstract

Abstract: An adaptive cubature Kalman filter(ACKF) algorithm is proposed to improve the filtering accuracy and stability of the cubature Kalman filter(CKF) algorithm when the passive sensor measurement noise is a mixed noise containing time-varying colored noise and jumping noise. Based on the basic CKF algorithm, the measurement reconstruction and undetermined coefficient decorrelation methods are used to derive the cubature Kalman filter algorithm with colored measurement noise (CKF-CMN). For the impaired filtering accuracy caused by time-varying colored noise and jumping noise, the idea of online correction of noise variance and removal of harmful measurement is introduced, and the ACKF algorithm is designed. The simulated results show that, compared with the basic CKF algorithm, the passive positioning accuracies of ACKF algorithm on x, y, and z axes are increased by 24.75%, 32.57% and 28.48%, respectively. The ACKF algorithm has higher filtering stability and accuracy.

Key words: mixednoise, cubatureKalmanfilter, adaptive, passivepositioning

CLC Number:

TJ765.3⁺35

CHEN Linxiu， HAO Mingrui， ZHAO Jiajia. Passive Target Positioning Algorithm for Mixed Noise[J]. Acta Armamentarii, 2021, 42(9): 1923-1930.

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