Waveform-agile sequential EKF for manoeuvrable target tracking

Since the cognitive radar was presented, dynamic waveform selection schemes have provided the optimal transmitted or received waveforms under certain criteria, based on the prior information of the target and environment, achieving the tracking performance improvement. This adaptive waveform solution has been paid more and more attention as advances in modern information processing techniques and devices enable its realization. In this paper, some improvements has been made to the existing dynamic waveform selection algorithms according to predecessor's work. The relationship between waveform parameters and tracking mean square error (MSE) is established through resolution cell and measurement extraction cell. According to this, the parameter of CF-HFM combined waveforms can be dynamically chosen to form the optimal transmitted waveform. A sequential extended kalman filter (SEKF) with Doppler measurements is used to track the manoeuvrable target. When the measurement model is nonlinear, this kind of selection and tracking algorithm can predict the target state directly thus reduce the computation. The proposed algorithm is validated to enhance the tracking performance by Monte Carlo simulations.