Polyphase orthogonal waveform optimization for MIMO-SAR using Genetic Algorithm

Mehany, Wael; Jiao, Licheng; Hussien, Khaled

doi:10.1117/12.2064053

Cited by 2 publications

(2 citation statements)

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“…Considering a GSFM pulse train with N sub-pulses un(t), the length of each pulse is L. The aperiodic correlation can be defined as [17]:R(p,q,k)={1Ltrue∑l=1L−kup(l)uq*(l+k),0≤k<L1Ltrue∑l=−k+1Lup(l)uq*(l+k),−L<k<0 where p,q=1,2,⋯,N, k is the discrete time index. When p=q, R(p,q,k) is the aperiodic auto-correlation function of pulse up…”

Section: Optimization Model Of Frequency-modulated Pulse Trainsmentioning

confidence: 99%

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Optimal Waveform Design Using Frequency-Modulated Pulse Trains for Active Sonar

Guan

Zhou

Zeng

2019

Sensors

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Frequency-modulated pulse trains can be applied in active sonar systems to improve the performance of conventional transmitted waveforms. Recently, two pulse trains have been widely researched as the transmitted waveforms for active sonars. The LFM-Costas pulse train was formed by modulating the linear frequency-modulated (LFM) waveform via the Costas sequence to remove the Doppler ambiguity of LFM pulses. The generalized sinusoidal frequency-modulated (GSFM) waveform, another frequency-modulated pulse train, achieved an ideal ambiguity function shape with thumbtack mainlobe. In this paper, we focus on constructing an optimization model to optimize the LFM-Costas and GSFM pulse trains with the genetic algorithm. The pulse trains can be improved on properties of both ambiguity function and correlations between sub-pulses. The optimized pulse trains are proven to have better detection performance than those of the initial pulse trains, including the lower sidelobe levels of ambiguity function, as well as lower cross-correlation property. Moreover, it is affirmed that the reverberation suppression performance of pulse trains has also been improved through the optimization model.

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Section: Optimization Model Of Frequency-modulated Pulse Trainsmentioning

confidence: 99%

“…Zhao proposed another approach for the optimal sidelobe design of the hopped-frequency waveform with the adaptive gradient search [16]. Moreover, Mehany and Wang both used the genetic algorithm (GA) to optimize orthogonal waveforms for radar systems [17,18].…”

Section: Introductionmentioning

confidence: 99%