A new approach for design of constant modulus discrete phase radar waveform with low WISL

Bu, Yi; Yu, Xianxiang; Yang, Jing; Fan, Tao; Cui, Guolong

doi:10.1016/j.sigpro.2021.108145

Cited by 21 publications

(6 citation statements)

References 43 publications

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“…The proposed approach was used to tackle the quadratic optimizations under unimodularity and similarity constraint (SC) for both continuous and discrete scenarios. In each iteration, the IADPM method converts the WISL optimization problem into two subproblems while locally increasing the considered penalty factor considered within the framework [33,34]. The authors showed that, compared to the developed CD and MM framework of [15], the IADPM has a better performance in autocorrelation suppression but consumes more computational resources.…”

Section: A Prior Artmentioning

confidence: 99%

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MaRLI: Attack on the Discrete-Phase WISL Minimization Problem

Eamaz,

Yeganegi,

Soltanalian

2024

IEEE Trans. Signal Process.

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To facilitate target localization, active radar signals or sequences are designed to have low auto-correlation. This goal is typically achieved by the minimization of the autocorrelation integrated sidelobe level (ISL) metric, or the more general, weighted version of the ISL, known as the WISL metric. In this paper, we introduce two problem formulations to address unimodular quartic program, which arises in numerous applications, with WISL minimization being one of them. The first approach is almost equivalent, while the second scheme utilizes a bi-quadratic formulation, both of which simplify the main problem to a quadratic program. We propose efficient approaches to WISL minimization for unimodular sequence design that take advantage of the low-cost and easily implementable power method-like iterations (PMLI). Moreover, to design the discrete-phase unimodular sequences with low WISL, the Mary unimodular sequence design via Relaxed PMLI (MaRLI) is proposed by changing and adopting the PMLI approach to tackle this challenging problem. Several numerical results and comparisons between MaRLI and other well-known sequence design algorithms are presented to illustrate the effectiveness of the proposed method. As we show, MaRLI appears to achieve a better suppression of the finite-alphabet autocorrelation levels compared to the state-of-the-art alternatives.

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Section: A Prior Artmentioning

confidence: 99%

“…Our main contributions in this paper are: 1) PMLI-based unimodular sequence design. This paper tackles the unimodular quartic programs (UQ 2 P) appearing in the many signal processing applications [2,3,19,34]. We address this problem using two distinct approaches.…”

Section: B Contributions Of the Papermentioning

confidence: 99%

MaRLI: Attack on the Discrete-Phase WISL Minimization Problem

Eamaz,

Yeganegi,

Soltanalian

2024

IEEE Trans. Signal Process.

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“…to formulate the areaing of these pre-suppressed range sidelobes interval, i.e., The classical weighted ISL given some prior information has been sed in [4,7,17], i.e., ( ) (5) orrowed the idea of (5), we denote { }…”

Section: { }mentioning

confidence: 99%

“…T to formulate the area-mapping of these pre-suppressed range sidelobes interval, i.e., z n = 1 when n ∈ R r , and z n = 0 when n / ∈ R r . The classical weighted ISL given some prior information has been discussed in [4,7,17], i.e.,…”

Section: { }mentioning

confidence: 99%

“…In mathematical sense, the MF output is usually the convolution between the received signal and the time-reversed replica of the transmitted signal, which is also regarded as the aperiodic auto-correlation [4][5][6]. Generally, to suppress range sidelobes which might obscure small targets of interest (especially, some dot targets or low RCS target) and further improve the anti-interference ability, the transmitting waveforms with desirable auto-correlation property are imperative by using some prior information [7][8][9][10]. Moreover, in engineering, the constant modulus (CM) of waveform (in most cases, i.e., unimodular property) could maximize the transmitter's efficiency, but also makes the mathematical problem of generating waveform be more non-convex [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

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Radar Phase-Coded Waveform Design with Local Low Range Sidelobes Based on Particle Swarm-Assisted Projection Optimization

Feng

Zhao

et al. 2022

Remote Sensing

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In modern electronic warfare, cognitive radar with knowledge-aided waveforms would show significant flexibility in anti-interference. In this paper, a novel method, named particle swarm-assisted projection optimization (PSAP), is introduced to design phase-coded waveforms with multi-level low range sidelobes, which mainly considers the stability for randomized initialization under the unimodular constraint. Firstly, the mathematical problem corresponding to avoid the range sidelobe masking from multiple non-cooperative targets or interference is formulated by giving different threat levels. Then, based on the alternating direction decomposition idea, the original problem is divided into triple-variable ones where these non-linear approximations can be solved via alternating projections along with FFT. Furthermore, the PSAP method with swarm intelligence, learning factor, and particle-assisted projection could ensure the optimization convergence in a parallel way, which could relax the non-convex constraint and enhance the global exploiting performance. Finally, simulations for several typical scenarios and numerical results are all provided to assess the waveforms generated by PSAP and other prevalent ones.

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