A NOVEL DATA-FUSION-BASED IMPROVEMENT TO DEBIASED CMKF TRACKING

A novel modification to the debiased convertedmeasurement Kalman filter (CMKF-D) is proposed and implemented. The resulting CMKF-D evaluates the average true measurement-error bias and covariance with polar target-position estimates obtained by nonlinearly transforming Cartesian estimates formed by traditional weighted-leastsquares fusion of the debiased converted measurements and the predicted Cartesian estimates of the CMKF-D. A tracking-performance comparison is made between the resulting CMKF-D and the previously presented CMKF-D which demonstrates the improvements obtained from the new technique when bearing measurement errors are large.