Decorrelated unbiased converted measurement for bistatic radar tracking

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