Fast Detection Method for Low-Observable Maneuvering Target via Robust Sparse Fractional Fourier Transform

Yu, Xiaohan; Chen, Xiaolong; Huang, Yong; Guan, Jian

doi:10.1109/lgrs.2019.2939264

Cited by 43 publications

(14 citation statements)

References 14 publications

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“…Therefore, in the background of the low SCR, the performance of the SFRFT algorithm will deteriorate, which will easily lead to false alarms. We combine the novelty of ADT-SFT in [27], and proposed the robust SFRFT (RSFRFT) [33], shown in Fig. 1, which is composed of seven procedures.…”

Section: B the Second Stage Detection Via Sparse Fractional Represenmentioning

confidence: 99%

“…Equ. (33) represents the ratio of target energy to clutter or noise energy, y(i) is the optimum FRFT domain or the best FRAF domain, and l is the maximum peak location, d = π f s sin α i 0 /N 2 is half of the peak width, f s is the sampling frequency.…”

Section: B Detection Comparisons With Frft-fraf-hcimentioning

confidence: 99%

“…8(b), it can be seen that FRFT and FRAF have certain energy concentration ability on the maneuvering target compared with MTD. The integration gain is improved, and the SCR out is calculated according to (33), i.e., SCR FRFT = 16.04 dB and SCR FRAF = 12.08 dB. Therefore, the FRFT corresponding to the maximum output SCR is selected as the second stage of the refined processing.…”

Section: B Detection Comparisons With Frft-fraf-hcimentioning

confidence: 99%

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Fast and Refined Processing of Radar Maneuvering Target Based on Hierarchical Detection via Sparse Fractional Representation

et al. 2019

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Reliable and fast detection of maneuvering target in complex background is important for both civilian and military applications. It is rather difficult due to the complex motion resulting in energy spread in time and frequency domain. Also, high detection performance and computational efficiency are difficult to balance in case of more pulses. In this paper, we propose a fast and refined processing method of radar maneuvering target based on hierarchical detection, utilizing the advantages of moving target detection (MTD), and the proposed sparse fractional representation. The method adopts two-stage threshold processing. The first stage is the coarse detection processing screening out the rangebins with possible moving targets. The second stage is called the refined processing, which uses robust sparse fractional Fourier transform (RSFRFT) or robust sparse fractional ambiguity function (RSFRAF) dealing with high-order motions, i.e., accelerated or jerk motion. And the second stage is carried out only within the rangebins after the first stage. Therefore, the amount of calculation can be greatly reduced while ensuring high detection performance. Finally, real radar experiment of UAV target detection is carried out for verification of the proposed method, which shows better performance than the traditional MTD method, and the FRFT-FRAF hierarchical coherent integration detection with less computational burden.

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Section: B the Second Stage Detection Via Sparse Fractional Represenmentioning

confidence: 99%

Section: B Detection Comparisons With Frft-fraf-hcimentioning

confidence: 99%

Section: B Detection Comparisons With Frft-fraf-hcimentioning

confidence: 99%

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Fast and Refined Processing of Radar Maneuvering Target Based on Hierarchical Detection via Sparse Fractional Representation

et al. 2019

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“…To solve the DFM effect problem, time-frequency techniques are widely employed, since they can directly compensate the time-varying Doppler frequency. A typical time-frequency technique is the fractional Fourier transform (FrFT), which decomposes the signal into a series of chirps, and the energy is concentrated on the matching fractional order [21]- [23]. Shorttime fractional Fourier transform (STFrFT) is another useful method, which represents the echo signal as chirp signals in a window function and gets good results [24], [25].…”

Section: Introductionmentioning

confidence: 99%

Weak Target Detection in the Presence of Sea Clutter Using Radon-Fractional Fourier Transform Canceller

Gao

Tao

Kang

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In this paper, we propose a new weak maneuvering target detection method in sea environment with Radonfractional Fourier transform (RFrFT) canceller, which can efficiently suppress the sea clutter and detect targets with low computations. To accomplish the sea clutter suppression, we design a canceller based on RFrFT in the detection process, which utilizes different representations of maneuvering target and sea clutter. A non-uniform searching strategy with nonuniform searching interval of fractional order is presented to reduce the computation of the detection process. Moreover, to make the RFrFT canceller also effective for multiple target case, we propose a band-stop design on the basis of the RFrFT convolution theorem by making use of the detection results after using the canceller. After that, the detailed detection algorithm is described. Experimental results on simulated and real data show that the proposed method is superior compared with other methods.

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“…The radar target detection research group from Naval Aviation University proposed several SFT-based moving target detection methods, i.e., adaptive dual-threshold sparse Fourier transform (ADT-SFT) [26] and robust SFRFT (RSFRFT) [27], which are more suitable for moving target detection in the clutter background. For highly maneuvering target detection, sparse fractional ambiguity function (SFRAF) [28] can not only achieve good aggregation of high-order phase signals by FRAF, but also achieve effective improvement of computational efficiency, and can realize rapid extraction of maneuvering targets under large data conditions.…”

mentioning

confidence: 99%

Adaptive Clutter Suppression and Detection Algorithm for Radar Maneuvering Target With High-Order Motions Via Sparse Fractional Ambiguity Function

Chen

Yu²,

Huang

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Radar maneuvering target detection in clutter background should not only consider the complex characteristics of the target to accumulate its energy as much as possible, but also suppress clutter to improve the signal-to-clutter ratio (SCR). The traditional fractional domain transform-based detection method requires parameters match searching, which costs heavy computational burden in case of a large amount of data. Sparse FT and sparse fractional FT can obtain high-resolution sparse representation of the target, but the signal sparsity needs to be known before, and the sparse representation performance is poor in clutter background. In this article, adaptive filtering method is introduced into the sparse fractional ambiguity function (SFRAF) method, and a SFRAF domain adaptive clutter suppression and highly maneuvering target detection algorithm is proposed, which is named as adaptive SFRAF (ASFRAF). The ASFRAF domain iterative filtering operation can suppress the clutter while retaining the signal energy as much as possible. Simulation results and measured radar data processing results show that the proposed algorithm can overcome the limitation of the SFRAF on the sparsity preset value and achieve high efficiency and robust detection of high-order phase maneuvering targets under a low SCR environment.

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Fast Detection Method for Low-Observable Maneuvering Target via Robust Sparse Fractional Fourier Transform

Cited by 43 publications

References 14 publications

Fast and Refined Processing of Radar Maneuvering Target Based on Hierarchical Detection via Sparse Fractional Representation

Fast and Refined Processing of Radar Maneuvering Target Based on Hierarchical Detection via Sparse Fractional Representation

Weak Target Detection in the Presence of Sea Clutter Using Radon-Fractional Fourier Transform Canceller

Adaptive Clutter Suppression and Detection Algorithm for Radar Maneuvering Target With High-Order Motions Via Sparse Fractional Ambiguity Function

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