TY - GEN
T1 - An improved pseudolinear Kalman filter algorithm for 3D Doppler-bearing target tracking
AU - Tao, Wenqi
AU - Wang, Xu
AU - Wang, Wenjing
AU - Wei, Guohua
N1 - Publisher Copyright:
© 2024 ACM.
PY - 2024/2/23
Y1 - 2024/2/23
N2 - This paper presents an instrumental-variable Kalman filter (IVKF) algorithm for three-dimensional (3D) Doppler-bearing target tracking. Initially, a 3D passive localization and tracking model is established for a nearly constant-velocity moving target using the angle of arrival (AOA) and Doppler frequency measurements obtained from a static observer. The pseudolinear Kalman filter (PLKF) algorithm is employed to achieve target tracking, and based on the bias-compensated PLKF (BC-PLKF), an IVKF algorithm is presented, which constructs IV matrixes to address biases resulting from the correlation between measurement vectors and pseudolinear noises. Simulation results demonstrate that the proposed recursive estimator has higher accuracy and robustness to initialization errors compared to traditional extended Kalman filter (EKF), with root mean squared error fairly close to the posterior Cramér-Rao lower bound (PCRLB).
AB - This paper presents an instrumental-variable Kalman filter (IVKF) algorithm for three-dimensional (3D) Doppler-bearing target tracking. Initially, a 3D passive localization and tracking model is established for a nearly constant-velocity moving target using the angle of arrival (AOA) and Doppler frequency measurements obtained from a static observer. The pseudolinear Kalman filter (PLKF) algorithm is employed to achieve target tracking, and based on the bias-compensated PLKF (BC-PLKF), an IVKF algorithm is presented, which constructs IV matrixes to address biases resulting from the correlation between measurement vectors and pseudolinear noises. Simulation results demonstrate that the proposed recursive estimator has higher accuracy and robustness to initialization errors compared to traditional extended Kalman filter (EKF), with root mean squared error fairly close to the posterior Cramér-Rao lower bound (PCRLB).
KW - Doppler-bearing tracking
KW - Instrumental variables
KW - Pseudolinear estimation
KW - Recursive target tracking
KW - Single static observer
UR - http://www.scopus.com/inward/record.url?scp=85201259550&partnerID=8YFLogxK
U2 - 10.1145/3653876.3653907
DO - 10.1145/3653876.3653907
M3 - Conference contribution
AN - SCOPUS:85201259550
T3 - ACM International Conference Proceeding Series
SP - 67
EP - 76
BT - 8th International Conference on Digital Signal Processing, ICDSP 2024 - Proceedings
PB - Association for Computing Machinery
T2 - 8th International Conference on Digital Signal Processing, ICDSP 2024
Y2 - 23 February 2024 through 25 February 2024
ER -