Maximum eigenvalue‐based target detection for the K‐distributed clutter environment

Zhao, Wenjing; Liu, Chang; Liu, Wenlong; Jin, Minglu

doi:10.1049/iet-rsn.2018.5229

Cited by 24 publications

(8 citation statements)

References 52 publications

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“…Their proposed model encompassed the multi pixel Adaptive Subspace Detector (ASD) along with the adaptive multipixel background-plus-noise power change detector for multi pixel target detection in sea clutter. Zhao et al [44] developed eigen value-based detection method where eigen values of the covariance matrix were used to calculate the correlation amongst the signal retrieved. However, for moving and small size object, its efficacy could not be justified.…”

Section: Related Workmentioning

confidence: 99%

Hanning Weighted Window Analysis Assisted Time-Series Analysis Model for Slow Moving Target Detection in Sea Clutter

Rajesh¹,

Udayarani²,

Jayaramaiah³

2020

International Journal of Engineering Research and Technology

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The exponential rise in maritime movement, security threats from up-surging terrorism and smuggling has alarmed academia-industries to develop more efficient method for target detection in coastal area. This task becomes more complex and intricate in case of small moving target in seaclutter. Amongst the major available approaches such as multiple-radar based methods, wavelet analysis methods, Doppler measurements, Fourier transforms, Short Term Fourier Transform (STFT) based techniques, STFT has been found more robust. The ability to perform simultaneous time and frequency analysis for time-series assessment enables STFT to be used in small target detection in sea clutter; however the inherent limitations such as improper Windowing, window-size, number of samples, overlapping conditions affect overall performance. Considering it as motivation, in this paper we have developed an efficient Hanning Weighted Window Function (HWWF) model to be used in conjunction with STFT to perform Hanning-Weighted Overlapped Time-Series Analysis (HWOTSA) to detect slow moving target detection in sea clutter. The use of orthonormal transformation also called rotation enabled Hanning Window Analysis to use suitable STFT parameters and statistical test across the windows to achieve better accuracy of the target detection. HWOTSA assisted STFT in conjunction with the Probability Distribution Function Projection (PPM) over extracted features enabled accurate slow moving target detection in sea clutter. The MATLAB based simulation affirmed that the proposed method enables accurate and swift (slow) moving target detection in sea clutter, without introducing higher computational overheads and complexities. The estimation of Doppler measurement based target velocity estimation armour proposed method to be used in real-time applications for swift decision making.

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Section: Related Workmentioning

confidence: 99%

Hanning Weighted Window Analysis Assisted Time-Series Analysis Model for Slow Moving Target Detection in Sea Clutter

Rajesh¹,

Udayarani²,

Jayaramaiah³

2020

International Journal of Engineering Research and Technology

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“…Formally, the problem of detecting radar moving target in the background of sea clutter plus noise can be represented by the following binary hypothesis model [23], [24]:…”

Section: Problem Formulationmentioning

confidence: 99%

“…Nevertheless, its heavy computational complexity limits its application in practical scenarios. The maximum eigenvalue based detection method (MEMD) is designed to achieve better performance with lower computational complexity in the strong clutter region [23]. While the detection performance of MEMD is inferior to the classical ANMF method when the target Doppler frequency heavily deviates from the clutter spectrum.…”

Section: A the Classical Cfar Detectorsmentioning

confidence: 99%

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Maximum Eigenvalue Matrix CFAR Detection Using Pre-Processing in Sea Clutter

Zhao

Jin

2019

IEEE Access

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For the pulse Doppler radar system with short pulse sequence, maximum eigenvalue-based matrix CFAR detector (MEMD) provides an effective solution to the problem of detecting moving targets in the background of K distribution sea clutter when the target Doppler frequency is within clutter spectrum. However, its performance is inferior to adaptive normalized matched filter method (ANMF) when the target Doppler frequency heavily deviates from the clutter spectrum. In essence, the reason for the better performance of ANMF method is the clutter is effectively suppressed by whitening and matched filter. Motivated by this, the fusion idea of eigenvalue detection and pre-processing via fast Fourier transform is considered taking advantage of the priori information of target steering vector. A pre-processing-based maximum eigenvalue matrix CFAR detection (P-MEMD) method is proposed to suppress the influence of clutter on detection performance. Finally, the numerical experimental results based on simulated data and real sea clutter data show that the proposed method achieves better detection performance and robustness than some conventional methods. INDEX TERMSMoving target detection, pre-processing, sea clutter.

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“…The Riemannian distance (RD) [14], [15] is the initial geometric measure in the matrix CFAR detection scheme, and it is applied in target detection with X-band and high frequency surface wave radars [10], [16], burg estimation of the scatter matrix [17] and the monitoring of wake vortex turbulences [18], [19]. To improve the detection performance and circumvent the expensive computation, the matrix CFAR detection scheme has been extended by replacing the RD with other different geometric measures, such as Kullback-Leibler divergence (KLD) [20], Jensen-Shanon divergence (JSD) [21], log-determinant divergence (LDD) [22] and their modified extension [23]- [25], etc [11], [26]. By these pioneered works, the geometry-based detection method has been widely investigated from both theoretical and practical perspective, and is developing to a critical and effective approach in detection issues because of the extraordinary detection performance and little requirement of the statistical characteristics of clutter environment [13], [27]- [30].…”

Section: Introductionmentioning

confidence: 99%

Dual Power Spectrum Manifold and Toeplitz HPD Manifold: Enhancement and Analysis for Matrix CFAR Detection

Wu¹,

Cheng²,

Chen³

et al. 2022

Preprint

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Recently, an innovative matrix CFAR detection scheme based on information geometry, also referred to as the geometric detector, has been developed speedily and exhibits distinct advantages in several practical applications. These advantages benefit from the geometry of the Toeplitz Hermitian positive definite (HPD) manifold M T 𝐻 ++ , but the sophisticated geometry also results in some challenges for geometric detectors, such as the implementation of the enhanced detector to improve the SCR (signal-to-clutter ratio) and the analysis of the detection performance. To meet these challenges, this paper develops the dual power spectrum manifold M P as the dual space of M T 𝐻 ++ . For each affine invariant geometric measure on M T 𝐻 ++ , we show that there exists an equivalent function named induced potential function on M P . By the induced potential function, the measurements of the dissimilarity between two matrices can be implemented on M P , and the geometric detectors can be reformulated as the form related to the power spectrum. The induced potential function leads to two contributions: 1) The enhancement of the geometric detector, which is formulated as an optimization problem concerning M T 𝐻 ++ , is transformed to an equivalent and simpler optimization on M P . In the presented example of the enhancement, the closed-form solution, instead of the gradient descent method, is provided through the equivalent optimization. 2) The detection performance is analyzed based on M P , and the advantageous characteristics, which benefit the detection performance, can be deduced by analyzing the corresponding power spectrum to the maximal point of the induced potential function.

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Maximum eigenvalue‐based target detection for the K‐distributed clutter environment

Cited by 24 publications

References 52 publications

Hanning Weighted Window Analysis Assisted Time-Series Analysis Model for Slow Moving Target Detection in Sea Clutter

Hanning Weighted Window Analysis Assisted Time-Series Analysis Model for Slow Moving Target Detection in Sea Clutter

Maximum Eigenvalue Matrix CFAR Detection Using Pre-Processing in Sea Clutter

Dual Power Spectrum Manifold and Toeplitz HPD Manifold: Enhancement and Analysis for Matrix CFAR Detection

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