Fast coherent integration for maneuvering target with high-order range migration via TRT-SKT-LVD

Li, Xiaolong; Cui, Guolong; Yi, Wei; Kong, Lingjiang

doi:10.1109/taes.2016.150573

Cited by 66 publications

(32 citation statements)

References 41 publications

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“…To this end, we have devised adaptive architectures which incorporate MOS rules to estimate the position and the extension of the target within the CPI. The performance analysis has highlighted the effectiveness of the proposed approach, since decision schemes based upon (9) can provide a significant performance gain with respect to the GAMF and the other herein proposed detectors. From the estimation point of view, the considered architectures can ensure a negligible RMSE for SINR values greater than 10 dB.…”

Section: Discussionmentioning

confidence: 88%

Adaptive Radar Detection of Dim Moving Targets in Presence of Range Migration

Addabbo

Orlando

Ricci

2019

IEEE Signal Process. Lett.

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This paper addresses adaptive radar detection of dim moving targets. To circumvent range migration, the detection problem is formulated as a multiple hypothesis test and solved applying model order selection rules which allow to estimate the "position" of the target within the CPI and eventually detect it. The performance analysis proves the effectiveness of the proposed approach also in comparison to existing alternatives.Index Terms-Adaptive radar detection, generalized likelihood ratio test, range migration, model order selection.

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Section: Discussionmentioning

confidence: 88%

Adaptive Radar Detection of Dim Moving Targets in Presence of Range Migration

Addabbo

Orlando

Ricci

2019

IEEE Signal Process. Lett.

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“…With the increasing requirements for space target detection and high-resolution imaging, radar high-speed maneuvering target detection has drawn growing attention [1][2][3][4][5][6][7][8][9][10][11]. Normally, a low-speed target is located in the same range cell during the short observation time, and the conventional moving target detection (MTD) algorithm [12] can complete coherent integration by using fast Fourier transform (FFT).…”

Section: Introductionmentioning

confidence: 99%

Coherent Integration for Radar High-Speed Maneuvering Target Based on Frequency-Domain Second-Order Phase Difference

et al. 2019

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In recent years, target detection has drawn increasing attention in the field of radar signal processing. In this paper, we address the problem of coherent integration for detecting high-speed maneuvering targets, involving range migration (RM), quadratic RM (QRM), and Doppler frequency migration (DFM) within the coherent processing interval. We propose a novel coherent integration algorithm based on the frequency-domain second-order phase difference (FD-SoPD) approach. First, we use the FD-SoPD operation to reduce the signal from three to two dimensions and simultaneously eliminate the effects of QRM and DFM, which leads to signal-to-noise ratio improvement in the velocity-acceleration domain. Next, we estimate the target motion parameters from the peak position without the need for a search procedure. We show that this algorithm can be easily implemented by using complex multiplications combined with fast Fourier transform (FFT) and inverse FFT (IFFT) operations. We perform comparisons with several representative algorithms and show that the proposed technique can be used to achieve a good trade-off between computational complexity and detection performance. We present both simulated and experimental data to demonstrate the effectiveness of the proposed method.

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“…In [8], the azimuth echo of fluctuating ships is modelled as a QFM signal, and the inverse synthetic aperture radar (ISAR) imaging is realised by accurately estimating the signal parameters. When achieving the coherent integration of manoeuvring target, the radar echoes are considered to be a QFM signal with respect to the slow time and better integration results are obtained [9]. Therefore, estimating the QFM signal parameters is of great significance in practical applications.…”

Section: Introductionmentioning

confidence: 99%