Abstract

Golay complementary waveforms can, in theory, yield radar returns of high range resolution with essentially zero sidelobes. In practice, when deployed conventionally, while high signal-to-noise ratios can be achieved for static target detection, significant range sidelobes are generated by target returns of nonzero Doppler causing unreliable detection. We consider signal processing techniques using Golay complementary waveforms to improve radar detection performance in scenarios involving multiple nonzero Doppler targets. A signal processing procedure based on an existing, so called, Binomial Design algorithm that alters the transmission order of Golay complementary waveforms and weights the returns is proposed in an attempt to achieve an enhanced illumination performance. The procedure applies one of three proposed waveform transmission ordering algorithms, followed by a pointwise nonlinear processor combining the outputs of the Binomial Design algorithm and one of the ordering algorithms. The computational complexity of the Binomial Design algorithm and the three ordering algorithms are compared, and a statistical analysis of the performance of the pointwise nonlinear processing is given. Estimation of the areas in the Delay–Doppler map occupied by significant range sidelobes for given targets are also discussed. Numerical simulations for the comparison of the performances of the Binomial Design algorithm and the three ordering algorithms are presented for both fixed and randomized target locations. The simulation results demonstrate that the proposed signal processing procedure has a better detection performance in terms of lower sidelobes and higher Doppler resolution in the presence of multiple nonzero Doppler targets compared to existing methods.

Highlights

  • Advanced signal processing techniques have long been used for radar detection to improve receiver signal-to-noise ratio (SNR) and target return range resolution

  • Its simplicity of generation and high time-bandwidth product make the linear frequency modulation (LFM) waveform an obvious choice [2]. Such LFM waveforms are well known to conflate range and Doppler as indicated by their ambiguity function (AF), and their target detection performance is limited by associated sidelobe issues

  • The computational complexity of the Binomial Design algorithm and the proposed three ordering algorithms described in the last section are examined; we study the condition at which the pointwise minimization can yield acceptable performance; thirdly, a method to estimate the areas in the Delay–Doppler map taken by range sidelobes based on knowledge of presented target is described

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Summary

Introduction

Advanced signal processing techniques have long been used for radar detection to improve receiver signal-to-noise ratio (SNR) and target return range resolution. Dang et al [32,33] proposed a Binomial Design (BD) algorithm that, in addition to re-ordering the waveforms, applies weights to the matched filtering This significantly expands the range sidelobe blanking area around zero-Doppler in the AF and reduces target detection uncertainty. We propose a new signal processing procedure to enhance radar illumination performance of multiple nonzero Doppler targets by applying a combination of the Reed–Müller code method of Suvorova et al and the Binomial Design algorithm of Dang et al via a pointwise nonlinear procedure. We estimate regions in the Delay–Doppler map in which there are significant range sidelobes, induced by the presence of targets, for each of the three ordering algorithms, in an attempt to improve separation of nonzero Doppler targets

Golay Complementary Waveforms in Reed–Müller Sequences
The Proposed Signal Processing Procedure
Binomial Design Algorithm
Algorithm 1
Algorithm 2
Algorithm 3
Pointwise Minimization
Analysis of the Proposed Signal Processing Procedure
Computational Complexity Comparison
Performance Analysis of the Pointwise Minimization
Estimation of Significant Range Sidelobe Regions in Delay–Doppler Map
Numerical Simulations and Discussion
Simulation under a Fixed Scenario
Simulation under a Randomized Scenario
Discussion:
Conclusions
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