ULA Fitting for Sparse Array Design

Shi, Wanlu; Vorobyov, Sergiy A.; Li, Yingsong

doi:10.1109/tsp.2021.3125609

Cited by 47 publications

(21 citation statements)

References 46 publications

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“…Following (7), the SCA can be regarded as a duplicate-andtransfer procedure, where one ULA serves as the prototype array to be duplicated and transferred and the other ULA decides transfer period and times. This point is in fact quite similar to that of the DCA discussed in [14]. In this letter, the DCA of the transmit array is selected as the prototype array, while the DCA of the receive array serves as the duplicateand-transfer (DT) array.…”

Section: Signal Model a The Dcsc Of Mimo Radarmentioning

confidence: 68%

“…In this regard, the UF principle is a good choice. In [14], the basic UF principle is discussed in detail and two SAs with low mutual coupling (UF-3BL and UF-4BL) are devised. An improved UF scheme is proposed in [15] for SA designs with high uDOF (ATLI-1BL).…”

Section: B Design Strategymentioning

confidence: 99%

“…where P DCA (x) indicates the DCA (positive set) of an SA, P dual DCA (x) implies the dual array (the detailed definition of dual array can be found in [14]) of P DCA (x), cons. {•} returns the consecutive part of a polynomial and omits the coefficients (for example, cons.…”

Section: B Design Strategymentioning

confidence: 99%

“…To reduce the mutual coupling, the uniform linear array fitting (UF) principle has been considered for sparse MIMO array designs. The UF principle is proposed to design sparse arrays (SAs) with desired property using combination of sub-ULAs, and two specific SAs (UF-3BL and UF-4BL) are devised with low mutual coupling [14]. Further, the improved UF scheme is proposed to increased the available uDOF [15].…”

Section: Introductionmentioning

confidence: 99%

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ULA Fitting for MIMO Radar

Shi¹,

Liu²,

Li³

2022

Preprint

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Nowadays, sparse multiple input and multiple output (MIMO) radar design considers the difference coarray of the sum coarray (DCSC) concept and the uniform degrees of freedom is improved significantly. Current sparse MIMO arrays are designed based on transmit and receive arrays with hole-free difference coarrays (DCAs). Recently, the uniform linear array (ULA) fitting (UF) principle is proposed which aims to design sparse arrays (SAs) with desired capacity using cascaded ULAs. However, SAs designed via UF can not guarantee hole-free DCAs. In this letter, we aim to use SAs designed via UF to devise sparse MIMO arrays with low mutual coupling. Strategies of using SAs with non hole-free DCA to design sparse MIMO arrays is introduced as well. Numerical simulations verifies the good performance of the proposed sparse MIMO array in high coupling scenarios.

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Section: Signal Model a The Dcsc Of Mimo Radarmentioning

confidence: 68%

Section: B Design Strategymentioning

confidence: 99%

Section: B Design Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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ULA Fitting for MIMO Radar

Shi¹,

Liu²,

Li³

2022

Preprint

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“…In addition, the adaptive interference suppression for PD radar can be considered using adaptive filtering [19][20][21][22][23][24][25][26][27][28] and sparse arrays [29][30][31].…”

Section: Simulation and Measurement Resultsmentioning

confidence: 99%

Speed Interference Suppression for PD Radar Based on Adaptive Dictionary

Zhang

et al. 2022

ACES Journal

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Random pulse initial phase (RPIP) signal is a kind of agility waveform which is commonly used in pulse Doppler (PD) radar. Although RPIP has the merit of restraining velocity deception jamming effectively, its efficiency is restricted under the condition of strong interference. To make the RPIP signal fully play the anti-jamming performance, this paper proposed a speed interference suppression method based on adaptive dictionar that separates the target echo from the strong jamming signal with good sparsity. First, the prior knowledge of strong interference signal is obtained by the technique of peak detection which is combined with the dual channel processing. Second, the quasi-Karhunen-Loeve transform (Q-KLT) basis of interference signal is constructed based on the prior knowledge, and the approximate Q-KLT basis of target signal is constructed by the way of dictionary learning, and those signals can be obtained from the adaptive dictionary by the algorithm of base tracking (BP). Finally, the effectiveness of the proposed method is verified by numerical simulation, which proves that the method can ensure a lower Doppler sidelobe in the strong interference scene, which confirmed that it has a good anti-velocity deception performance.

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