Performance of time delay estimation in a cognitive radar

TL;DR

This paper analyzes a sub-Nyquist cognitive radar system, deriving bounds on delay estimation accuracy, optimizing band selection and power distribution, and demonstrating improved performance over conventional radar especially at low SNRs.

Contribution

It provides theoretical bounds and optimization strategies for delay estimation in sub-Nyquist cognitive radar, enhancing understanding of its performance advantages.

Findings

Equi-width subbands improve delay estimation over conventional radar.

Cognitive radar performs better in low SNR regions.

Derived bounds guide optimal band and power allocation.

Abstract

A cognitive radar adapts the transmit waveform in response to changes in the radar and target environment. In this work, we analyze the recently proposed sub-Nyquist cognitive radar wherein the total transmit power in a multi-band cognitive waveform remains the same as its full-band conventional counterpart. For such a system, we derive lower bounds on the mean-squared-error (MSE) of a single-target time delay estimate. We formulate a procedure to select the optimal bands, and recommend distribution of the total power in different bands to enhance the accuracy of delay estimation. In particular, using Cram\'er-Rao bounds, we show that equi-width subbands in cognitive radar always have better delay estimation than the conventional radar. Further analysis using Ziv-Zakai bound reveals that cognitive radar performs well in low signal-to-noise (SNR) regions.