Abstract
A new scheme based on Kalman filtering to optimize the waveforms of an adaptive multi-antenna radar system for target impulse response (TIR) estimation is presented. This work aims to improve the performance of TIR estimation by making use of the temporal correlation between successive received signals, and minimize the mean square error (MSE) of TIR estimation. The waveform design approach is based upon constant learning from the target feature at the receiver. Under the multiple antennas scenario, a dynamic feedback loop control system is established to real-time monitor the change in the target features extracted form received signals. The transmitter adapts its transmitted waveform to suit the time-invariant environment. Finally, the simulation results show that, as compared with the waveform design method based on the MAP criterion, the proposed waveform design algorithm is able to improve the performance of TIR estimation for extended targets with multiple iterations, and has a relatively lower level of complexity.
Highlights
Cognitive radars have received a lot of attention in recent years
The radar system updates the target impulse response (TIR) estimation and utilizes this information to choose the optimal waveform for transmission
An adaptive feedback loop enables the delivery of the estimated value of TIR to the transmitter
Summary
Cognitive radars have received a lot of attention in recent years. Similar to brain-empowered system architectures, cognitive radar employs adaptive feedback principle to facilitate adaptive detection of the time-invariant target scene. The target feature information in the backscatter signal is exploited to allocate the power or spectrum of the probing signal at the transmitter [1,2]. Cognitive radar usually forms a closed feedback loop from the receiver to the transmitter. It is able to adaptively adjust probing signals or the receiver to suit the time variant target scene. The feedback loop has great potentials in improving the performance of target recognition and detection, as shown in [3,4]
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