Radar Moving Target Detection in Clutter Background via Adaptive Dual-Threshold Sparse Fourier Transform

Yu, Xiaohan; Chen, Xiaolong; Huang, Yong; Zhang, Lin; Guan, Jian; He, You

doi:10.1109/access.2019.2914232

Cited by 51 publications

(17 citation statements)

References 18 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%

“…7. The N 2 ∈ [2, N ] sampled pulses of the same rangebin in the output of the first stage are performed FRFT and FRAF operations under different transform angle conditions, i.e., (27) where α ∈ (−π, π] is the rotation angle, and τ 0 is a constant indicating the delay, which is determined by the initial distance r 0 of the target to be detected, i.e., τ 0 = 2r 0 /c, K α (t m , u) represents the kernel function.…”

Section: B Detection Comparisons With Frft-fraf-hcimentioning

confidence: 99%

“…However, most of the methods are mainly for signal processing in noise background, and the SFT is easily affected by strong clutter. Ref [27] designed an adaptive dual-threshold SFT (ADT-SFT) algorithm, which is used for uniformly moving target detection in clutter environment.…”

Section: Introductionmentioning

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%

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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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“…Its existence seriously interferes with the detection and tracking performance of radar targets on the sea surface These methods mainly involve the combination of traditional moving target detection (MTD) and adaptive detection technology, among which, space-time adaptive processing (STAP) [14] and constant false alarm rate (CFAR) detection are classic methods of clutter suppression and target detection, respectively, and many results have achieved the detection of large-size and high-strength targets [15]- [18]. For example, Yu et al [19] proposed an adaptive dualthreshold sparse Fourier transform (ADT-SFT) algorithm, and experiments demonstrated that the ADT-SFT algorithm is more suitable for the clutter background and can obtain a better detection performance than SFT and robust SFT. Magraner et al [20] presented a new technique based on cellaveraging CFAR detection, and it achieved good detection results.…”

Section: Introductionmentioning

confidence: 99%

Clutter Suppression and Target Tracking by the Low-Rank Representation for Airborne Maritime Surveillance Radar

et al. 2020

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Moving target detection is of vital importance to maritime security and maritime resource protection. However, the detection of slow or weak targets is difficult based on traditional methods. A new detection method is proposed by using the different motion variations of radar moving target and sea clutter in the range-Doppler spectrum sequence. The first step in implementing this method is the separation of moving target and sea clutter by the low-rank representation, in which the target and clutter are modeled as foreground and background components. Subsequently, a sea clutter discriminator is constructed within the sea clutter bandwidth to further remove the sea clutter (false alarms) that exists in the foreground. The proposed method can reduce the sea clutter power while maintaining the target power and improve the detection rate of moving targets, especially slow or weak targets. Data collected with airborne maritime surveillance radar in maritime moving target indication (MMTI) mode are used to validate the performance of the proposed method. The experimental results demonstrate that the improvement in the signal-to-clutter ratio (SCR) obtained with the proposed method is better than that obtained with space-time adaptive processing (STAP, including 1DT-STAP, 3DT-STAP and sparse-STAP) and principal component pursuit (PCP) methods; additionally, the figure of merit (FOM) of the proposed method is higher than that of the constant false alarm rate (CFAR) and PCP method. Furthermore, the tracks of ships are obtained by applying a location constraint to the foreground sequence.

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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.…”

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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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Radar Moving Target Detection in Clutter Background via Adaptive Dual-Threshold Sparse Fourier Transform

Cited by 51 publications

References 18 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

Clutter Suppression and Target Tracking by the Low-Rank Representation for Airborne Maritime Surveillance Radar

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

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