Waveform design for TDM-MIMO radar systems

Alaie, M. Bagher; Olamaei, Seyed Ahmad

doi:10.1016/j.sigpro.2019.107307

Cited by 8 publications

(8 citation statements)

References 25 publications

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“…It is evident that the MWISL cannot suppress sidelobes down to almost zero for L = 11 and L = 15, while the MeSiCA can do the task for all scenarios. [5,9,11,15] sidelobes when N = 20.…”

Section: ) Comparison With Barker (Suppressing More Than Half Of the Sidelobes)mentioning

confidence: 99%

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Designing Sequences With Minimized Mean Sidelobe Level for Cognitive Radars

2021

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In this paper, a set of sequences is developed with a minimized mean sidelobe level (MSL). The problem is formulated for cognitive radars using the l 1 -norm, where the target cognition determines which sidelobes should be suppressed. The cognitive radar configuration requires fast waveform regeneration in each cognition cycle. In this light, the computational burden of the algorithms developed here is revealed to be situated on the singular value decomposition (SVD) operation. The randomization method is adopted to speed up the proposed algorithms. The obtained fast generation of sequences with the desired autocorrelation is key to its utilization in cognitive radars. We also consider two practically important cases and accommodate the proposed approach to them: unimodular and finite-alphabet sequences. The superiority of the developed algorithms is confirmed both in suppression level and speed through extensive numerical simulations.INDEX TERMS Cyclic algorithm, cognitive radar, mean sidelobe level, radar waveform design.

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“…It is evident that the MWISL cannot suppress sidelobes down to almost zero for L = 11 and L = 15, while the MeSiCA can do the task for all scenarios. [5,9,11,15] sidelobes when N = 20.…”

Section: ) Comparison With Barker (Suppressing More Than Half Of the Sidelobes)mentioning

confidence: 99%

“…Generally, the suppression degree is measured by a number of metrics including integrated sidelobe level (ISL) [5], [9]- [11], peak sidelobe level (PSL) [12], [13] and mean sidelobe level (MSL) [14]- [18]. These metrics can be considered for both autocorrelation [19] and ambiguity function (AF) [20], [21].…”

Section: Introductionmentioning

confidence: 99%

Designing Sequences With Minimized Mean Sidelobe Level for Cognitive Radars

2021

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“…Recently, the great potential of multiple-input multiple-output (MIMO) radars [1][2][3] has attracted considerable attention in terms of waveform design and diversity [4][5][6][7][8][9]. Unlike traditional phased-array radars, MIMO radars transmit multiple orthogonal signals via several transmitters.…”

Section: Introductionmentioning

confidence: 99%

A Novel MIMO Radar Orthogonal Waveform Design Algorithm Based on Intelligent Ions Motion

Zhang

Wen

2021

Remote Sensing

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Orthogonal waveform design is one of the key technologies that affects the detection performance of MIMO radars. Most of the existing methods indirectly tackle this problem as an intractable nonconvex optimization and an NP-hard problem. In this work, we propose a novel waveform design algorithm based on intelligent ions motion optimization (IMO) to directly obtain a set of polyphase codes with good orthogonality. The autocorrelation sidelobe and cross-correlation sidelobe are first derived and subsequently integrated into evaluation functions for evaluating the orthogonality of polyphase codes. In order to effectively cope with the aforementioned problem, we present a strengthened IMO that is highly robust and converges rapidly. In the liquid state, an optimal guiding principle of same-charge ions is suggested to enhance global search ability and avoid falling into local optima. An ion updating strategy based on fitness ranking is presented to improve the search efficiency in the crystal state. Finally, the improved algorithm is employed to optimize the polyphase codes. The experimental results, compared with other state-of-the-art algorithms, show that the polyphase codes obtained by the proposed algorithm have better orthogonality.

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“…New sequence set design methods with accurately controlled autocorrelation and cross-correlation sidelobe levels were developed in [14] for the design of unimodular sequences with accurately controlled correlation properties within the lag interval of interest. The concept of majorization-minimization in combination with the second order cone programming and semidefinite programming was used in [15] to design waveforms for Time-Division-Multiplexing (TDM) MIMO radar systems. Using a different approach, a frequency domain expression was presented in [16] for the waveform autocorrelation.…”

Section: Introductionmentioning

confidence: 99%

Design of Matched and Mismatched Filters Based on Peak Sidelobe Level Minimization

Abouei¹,

Pishrow²,

Ghaferi³

2021

RADIOENGINEERING

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This paper focuses on the design of matched filters with low peak sidelobe level as well as mismatched filters with low loss in processing gain and peak sidelobe level, for phase codes. We propose an algorithm which employs the least-p th norm minimax based on the genetic algorithm, and a method based on the semidefinite programming to deal respectively with the resulting matched and mismatched optimization problems. A framework is also presented to design mismatched filters that are robust to Doppler shifts. Simulation results show that using the proposed methods for finding matched filters leads to better peak sidelobe level and integrated sidelobe level for binary and polyphase codes compared to previous works. In addition, the mismatched filters designed by the proposed methods have very low peak sidelobe level in the binary and polyphase cases.

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Waveform design for TDM-MIMO radar systems

Cited by 8 publications

References 25 publications

Designing Sequences With Minimized Mean Sidelobe Level for Cognitive Radars

Designing Sequences With Minimized Mean Sidelobe Level for Cognitive Radars

A Novel MIMO Radar Orthogonal Waveform Design Algorithm Based on Intelligent Ions Motion

Design of Matched and Mismatched Filters Based on Peak Sidelobe Level Minimization

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