TY - GEN
T1 - An Efficient Batch Bayesian WIV Doppler and Bearing Estimator for 3D Target Tracking
AU - Du, Chang
AU - Ren, Jinlong
AU - Wei, Guohua
N1 - Publisher Copyright:
© 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.
PY - 2023/2/17
Y1 - 2023/2/17
N2 - The paper focuses on the 3D TMA problem of a tough target-observer geometry where the observer is motionless and the target is approaching at a constant speed. We first formulate the pseudolinear equations using bearings, elevations and Doppler frequency and derive the measurement noise covariance matrix, which is used as the weighted matrix of the estimator, much more complex than that in 2D. An efficient batch Bayesian filtered WIV estimator is then proposed, based on the principle of instrumental variables and mean filtering to the measurements. Simulation results illustrate that the estimating bias tends to be small as the number of instants increases and the noise fluctuation decreases, compared with the basic weighted pseudolinear estimator. And the RMSE fits the Cramér-Rao Bound for small levels of noise. It is verified that the proposed estimator is effective in reducing bias and improving estimating accuracy in 3D TMA.
AB - The paper focuses on the 3D TMA problem of a tough target-observer geometry where the observer is motionless and the target is approaching at a constant speed. We first formulate the pseudolinear equations using bearings, elevations and Doppler frequency and derive the measurement noise covariance matrix, which is used as the weighted matrix of the estimator, much more complex than that in 2D. An efficient batch Bayesian filtered WIV estimator is then proposed, based on the principle of instrumental variables and mean filtering to the measurements. Simulation results illustrate that the estimating bias tends to be small as the number of instants increases and the noise fluctuation decreases, compared with the basic weighted pseudolinear estimator. And the RMSE fits the Cramér-Rao Bound for small levels of noise. It is verified that the proposed estimator is effective in reducing bias and improving estimating accuracy in 3D TMA.
KW - 3D Doppler-bearing problems
KW - Instrumental variables
KW - Pseudolinear estimator
KW - Target motion analysis (TMA)
UR - http://www.scopus.com/inward/record.url?scp=85163838301&partnerID=8YFLogxK
U2 - 10.1145/3585542.3585555
DO - 10.1145/3585542.3585555
M3 - Conference contribution
AN - SCOPUS:85163838301
T3 - ACM International Conference Proceeding Series
SP - 86
EP - 94
BT - Proceedings of the 2023 7th International Conference on Digital Signal Processing, ICDSP 2023
PB - Association for Computing Machinery
T2 - 7th International Conference on Digital Signal Processing, ICDSP 2023
Y2 - 17 February 2023 through 19 February 2023
ER -