Decorrelated unbiased converted measurement for bistatic radar tracking

Bistatic radar target tracking is challenging due to the fact that the measurements are nonlinear functions of the Cartesian state. The converted measurement Kalman filter (CMKF) converts the raw measurement into Cartesian coordinates prior to tracking and is superior to the extended Kalman filter for certain problems. The challenges of CMKF are debiasing the converted measurement and approximating the converted measurement error covariance. Due to no closed form of biases, we utilize the second-order Taylor series expansion of the conventional measurement conversion to find the conversion bias in bistatic radar and propose the unbiased converted measurement (UCM). In order to decorrelate the converted measurement error covariance from the measurement noise, we evaluate the covariance using the prediction in Bayesian recursive filtering, designated as the decorrelated unbiased converted measurement (DUCM). Monte Carlo simulations show that the DUCM is unbiased and consistent, and the DUCM filter exhibits an improved performance compared with the conventional CMKF and the UCM filter in bistatic radar tracking. (C) 2021 Society of Photo-Optical Instrumentation Engineers (SPIE)

Automated summary

Bistatic radar target tracking is challenging due to nonlinear measurements of Cartesian state. The study introduces Converted Measurement Kalman Filter (CMKF) and proposes Unbiased Converted Measurement (UCM) and Decorrelated Unbiased Converted Measurement (DUCM) to address challenges in CMKF. Monte Carlo simulations show that the DUCM filter outperforms the traditional CMKF and UCM filters in bistatic radar tracking.