Ambiguity Function Shaping based on Alternating Direction Riemannian Optimal Algorithm

TL;DR

This paper introduces a novel waveform design method for cognitive radar that minimizes interference by optimizing the slow-time ambiguity function using an alternating direction Riemannian algorithm, enhancing radar adaptability.

Contribution

It proposes an innovative iterative algorithm within an ADPM framework combining convex solutions and Riemannian trust region methods for waveform synthesis.

Findings

Outperforms existing algorithms in STAF, range-cut, and SIR.

Effectively suppresses interference power in waveform design.

Enhances cognitive radar's environmental adaptability.

Abstract

In order to improve the ability of cognitive radar (CR) to adapt to the environment, the required ambiguity function (AF) can be synthesized by designing the waveform. The key to this problem is how to minimize the interference power. Suppressing the interference power is equivalent to minimize the expectation of slow-time ambiguity function (STAF) over range-Doppler bins. From a technical point of view, this is actually an optimization problem of a non-convex quartic function with constant modulus constraints (CMC). In this paper, we proposed a novel method to design a waveform to synthesize the STAF based on suppressing the interference power. We put forward an iterative algorithm within an alternating direction penalty method (ADPM) framework. In each iteration, this problem is split into two sub-problems by introducing auxiliary variables. In the first sub-problem, we solved the convex problem directly with a closed-form solution, then utilized the Riemannian trust region (RTR) algorithm in the second sub-problem. Simulation results demonstrate that the proposed algorithm outperforms other advanced algorithms in the aspects of STAF, range-cut and signal-to-interference-ratio (SIR) value.