Closed-form CRLB and LPI power allocation strategies for MIMO radars with unneglectable clutter

Kafshgari, Shaghayegh; Andargoli, Seyed Mehdi Hosseini

doi:10.1016/j.phycom.2023.102011

Cited by 2 publications

(2 citation statements)

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“…In 2023, Stefano Buzzi et al (from Italy) derived a GLRT receiver for unknown target response, and solved the design of introducing phase shift in reflective components in MIMO radar systems by maximizing the target detection probability with fixed false alarm probability [16]. Shaghayegh Kafshgari et al proposed four optimized power allocation (PA) strategies based on the closed form CRLB of MIMO radar system positioning, taking measurement error statistics as an objective function or constraint so as to improve the radar positioning performance [17]. In 2023, K Xiong et al studied the localization problem of the MIMO radar with a wide separation directional transmitter and omnidirectional receiver, and proposed a distributed localization framework for the MIMO radar based on a mixed measurement of incident angle and bistatic distance.…”

Section: Introductionmentioning

confidence: 99%

“…In general, regarding the numerical solutions to the passive DPD method for the MIMO radar emitter, one basic approach is to employ the intrinsic system characteristics of the MIMO radar to improve the localization accuracy, such as spatial waveform diversity gain and array error correction [14,15,17]. Other commonly used methods combine the MIMO technique with other technologies in specific application contexts, such as synthetic aperture technology and time-frequency analysis [13,18].…”

Section: Introductionmentioning

confidence: 99%

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Passive Direct Position Determination Based on KL Transform and Feature Matching

2023

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In this article, the problem of simultaneously estimating and localizing multiple-input multiple-output (MIMO) radar emitters is considered for a distributed multi-station passive localization system, wherein the transmitted signal is unknown for receiver stations. To achieve highly accurate and robust localization performance, a novel algorithm based on the direct position determination (DPD) algorithm, Karhunen–Loève (KL) transform, and feature matching (FM) is addressed to jointly estimate the emitter position and the unknown signal waveform. First, we further derive the objective function of the DPD method and present an enhanced strategy to exploit as much waveform information as possible without any prior knowledge. By applying KL transform and FM techniques, the proposed method achieves MIMO radar emitter identification and emitter localization. The numerical results show that the proposed algorithm outperforms the existing DPD approaches which ignore the transmitted signals, especially for a low signal-to-noise ratio (SNR).

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Section: Introductionmentioning

confidence: 99%