An Efficient Batch Bayesian WIV Doppler and Bearing Estimator for 3D Target Tracking
Chang Du, Jinlong Ren, Guohua Wei · 2023
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.