High-Speed Target Detection Algorithm Based on Sparse Fourier Transform

Pang, Cunsuo; Liu, Shengheng; Han, Yan

doi:10.1109/access.2018.2853180

Cited by 36 publications

(18 citation statements)

References 26 publications

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“…The reason is that the way of binary search is better than voting method under the large noisy situation. And the way of multiscale search is not good when it use in noisy situation according to the formula (26) and Figure 3.…”

Section: B Comparison Experiments About Different Algorithms Using Thmentioning

confidence: 99%

“…The paper [17] proposes an overview of sFFT technology and summarizes a three-step approach in the stage of spectrum reconstruction and provides a standard testing platform that can be used to evaluate different sFFT algorithms. There are also some researches try to conquer the sFFT problem from a lot of aspects: computational complexity [18], [19], performance of the algorithm [20], [21], software [22], [23], higher dimensions [24], [25], implementation [26], hardware [27] and special setting [28], [29] perspectives.…”

Section: Introductionmentioning

confidence: 99%

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On Performance of Sparse Fast Fourier Transform Algorithms Using the Flat Window Filter

Jiang

Chen

2020

IEEE Access

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The problem of computing the Sparse Fast Fourier Transform(sFFT) of a K-sparse signal of size N has received significant attention for a long time. The first stage of sFFT is hashing the frequency coefficients into B(≈ K) buckets named frequency bucketization. The process of frequency bucketization is achieved through the use of filters: Dirichlet kernel filter, aliasing filter, flat filter, etc. The frequency bucketization through these filters can decrease runtime and sampling complexity in low dimensions. It is a hot topic about sFFT algorithms using the flat filter because of its convenience and efficiency since its emergence and wide application. The next stage of sFFT is the spectrum reconstruction by identifying frequencies that are isolated in their buckets. Up to now, there are more than thirty different sFFT algorithms using the sFFT idea as mentioned above by their unique methods. An important question now is how to analyze and evaluate the performance of these sFFT algorithms in theory and practice. In this paper, it is mainly discussed about sFFT algorithms using the flat filter. In the first part, the paper introduces the techniques in detail, including two types of frameworks, five different methods to reconstruct spectrum and corresponding algorithms. We get the conclusion of the performance of these five algorithms, including runtime complexity, sampling complexity and robustness in theory. In the second part, we make three categories of experiments for computing the signals of different SNR, different N , and different K by a standard testing platform and record the run time, percentage of the signal sampled, and L 0 , L 1 , L 2 error both in the exactly sparse case and general sparse case. The result of experiments is consistent with the inferences obtained in theory. It can help us to optimize these algorithms and use them correctly in the right areas.

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Section: B Comparison Experiments About Different Algorithms Using Thmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On Performance of Sparse Fast Fourier Transform Algorithms Using the Flat Window Filter

Jiang

Chen

2020

IEEE Access

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“…3 that, the DFT magnitude spectrum of the undersampled sequences shows sparsity. Thus, sparse Fourier transform [31] can be potentially used to further reduce the computational complexity of frequency estimation. Step 3: In terms of (28), D peak indicators are determined as c 1 = 4, c 2 = 3, c 3 = 1, c 4 = 2.…”

Section: A Stepwise Demo Of the Multitarget Sequential Estimatormentioning

confidence: 99%

Sequential Estimation of Frequency and Direction-of-Arrival Based on the Relaxed Coprime Array for Multiple Targets

2020

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Recently, extensive researches focus on the joint estimation of carrier frequency and directionof-arrival, whereas the heavy computation burden and affordable hardware cost are always required therein. To alleviate the problem, we propose a relaxed coprime array based two-stage estimator, which can sequentially detect the frequencies and direction-of-arrival for multiple targets. Guided by the closed-form robust algorithm, this relaxation yields a higher array sparsity compared with existing sparse arrays. Specifically, the first stage aims to achieve the classification of the frequency and angle remainders arising from temporal-spatial undersampling, which is realized by combining spectrum correction with pattern clustering. By incorporating the closed-form robust Chinese Remainder Theorem into the remainder classification result, we obtain all the frequencies and direction-of-arrival in the second stage. Both performance analysis and numerical results were conducted to verify the effectiveness of the proposed estimator in the multi-target case.

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“…For a sparse N -point size input signal, the computational complexity of SFT can be reduced to O(K log N ) compared with FFT, where K is called as the sparsity, i.e., the number of large-valued coefficients in the frequency domain [22], [23]. Ref [24] proposed a method for high-speed target detection based on SFT. However, the SFT needs to know signal sparsity in advance, which is impossible and unrealistic.…”

Section: Introductionmentioning

confidence: 99%

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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High-Speed Target Detection Algorithm Based on Sparse Fourier Transform

Cited by 36 publications

References 26 publications

On Performance of Sparse Fast Fourier Transform Algorithms Using the Flat Window Filter

On Performance of Sparse Fast Fourier Transform Algorithms Using the Flat Window Filter

Sequential Estimation of Frequency and Direction-of-Arrival Based on the Relaxed Coprime Array for Multiple Targets

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

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