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According to the biased angles provided by the bistatic sensors,the necessary condition of observability and Cramer-Rao low bounds for the bistatic system are derived and analyzed,respectively. Additionally,a dual Kalman filter method is presented with the purpose of eliminating the effect of biased angles on the state variable estimation. Finally,Monte-Carlo simulations are conducted in the observable scenario. Simulation results show that the proposed theory holds true,and the dual Kalman filter method can estimate state variable and biased angles simultaneously. Furthermore,the estimated results can achieve their Cramer-Rao low bounds.
According to the biased angle provided by the bistatic sensors, the necessary condition of observability and cramer-Rao low bounds for the bistatic system are derived and analyzed, respectively. Additionally, a dual Kalman filter method is presented with the purpose of eliminating the effect of Finally, Monte-Carlo simulations are conducted in the observational scenario. Simulation results show that the proposed theory holds true, and the dual Kalman filter method can estimate state variable and biased angles simultaneously. results can achieve their Cramer-Rao low bounds.