Abstract
In this paper, we propose robust waveform techniques for multistatic cognitive radars in a signal-dependent clutter environment. In cognitive radar design, certain second order statistics such as the covariance matrix of the clutter, are assumed to be known. However, exact knowledge of the clutter parameters is difficult to obtain in practical scenarios. Hence, we consider the case of waveform design in the presence of uncertainty on the knowledge of the clutter environment and propose both worst-case and probabilistic robust waveform design techniques. Initially, we tested our multistatic, signal-dependent model against existing worst-case and probabilistic methods. These methods appeared to be over conservative and generic for the considered scenario. We therefore derived a new approach where we assume uncertainty directly on the radar cross-section and Doppler parameters of the clutters. Accordingly, we propose a clutter-specific stochastic optimization that, by using Taylor series approximations, is able to determine robust waveforms with specific signal to interference and noise ratio outage constraints.
Highlights
Cognitive radars are based on three main concepts: intelligent signal processing; feedback from the receiver to the transmitter and preservation of the information content of radar returns [1]
In a Cognitive Radar Network (CRN), several radars could work together in a cooperative manner [9] combining the benefits of both cognition and diversity offered by Multiple Input Multiple Output (MIMO) radars
It needs to be noted that a problem that becomes infeasible in the inner loop for a specific SINRmin might provide again feasible results after the receiver filter optimization is performed in the outer loop
Summary
Cognitive radars are based on three main concepts: intelligent signal processing; feedback from the receiver to the transmitter and preservation of the information content of radar returns [1]. Radar waveform design should take into account uncertainties associated with clutter parameters This can be done in two ways: with worst-case optimization techniques [21] and with stochastic optimization techniques [22]. The first two techniques employ traditional worst-case optimization and probabilistic (stochastic) optimization, respectively Both methods are used for robust radar waveform design in the presence of uncertainty on the clutterplus-noise covariance matrix. The third technique considers a novel approach where the uncertainty is assumed directly on the radar cross-section and Doppler parameters of the clutter rather than on the estimated clutter-plus-noise covariance matrix. The latter is solved using Taylor approximations and stochastic optimization.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
