Fast scan‐to‐scan integration algorithm for small target in sea clutter

Jin, Benzhou; Ma, Yongxue; Wu, Gang

doi:10.1049/el.2017.1312

Cited by 7 publications

(16 citation statements)

References 3 publications

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“…Target detection on one azimuth beam uses radar data at multiple contiguous range cells and multiple consecutive scans. The multiscan joint detection boils down to the following binary hypothesis test problem [18,19], 0 1 H : ( , , ) ( , , ) H : ( , , ) ( , , ) ( , , ) 1, 2, ; 1,..., ; 1,..., .…”

Section: A Signal Model For Multiscan Joint Detectionmentioning

confidence: 99%

“…And then, the interscan binary integration using the retrospective filter bank (RFB) is used for final decision. In [19], the generalized likelihood ratio test with linear threshold detector (GLRT-LTD), which is the optimum coherent detector in compound-Gaussian sea clutter with the inverse Gamma-distributed textures [40], is used as the intrascan processing to generate the plot data, their test statistic and Doppler offsets. Stacking these data on the range-scan time plane in the interscan processing, the three features are extracted by the integration using the Dopplerguided retrospective filter for final decision.…”

Section: B Multiscan Joint Detection Framework At Fast-scan Modementioning

confidence: 99%

“…The work in [19] mentioned the inconsistency between the Doppler offset of sea clutter in the scale of radar slow time and the movement of structural texture of sea clutter in the scale of radar scan time but does not go deep into cross-scale modeling of sea clutter. It is important to develop cross-scale sea clutter model from radar slow time to scan time for multiscan joint target detection in fast-scan maritime radars.…”

Section: B Multiscan Joint Detection Framework At Fast-scan Modementioning

confidence: 99%

“…In [19], the intrascan optimum coherent integration under the generalized Pareto distribution of sea clutter and the multiple feature integration via Doppler guided retrospective filters are combined to form a multiscan joint detection scheme and it first exploits the inconsistency between the Doppler offset of sea clutter in intrascan coherent processing interval (CPI) of tens of milliseconds and the interscan radial movement of swells and large-scale waves. It attains obvious improvement in detection of slow-moving small targets in high-resolution sea clutter with structural textures.…”

Section: Introductionmentioning

confidence: 99%

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Small Target Detection Based on Noncoherent Radial Velocity Spectrum of High-Resolution Sea Clutter

Shi

Shui

Liang

et al. 2022

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

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This paper proposes a multiscan joint non-coherent detection method of small targets in high-resolution sea clutter with spatial-temporally structural textures induced by large-scale swells and waves, which is composed of intrascan non-coherent integration at each spatial resolution cell followed by interscan integration on a radial velocity template. The main contributions of the paper are as follows. Firstly, a concept of non-coherent radial velocity spectrum (NCRVS) is proposed to model the nonhomogeneity of multiscan integration of sea clutter with structural textures. In the NCRVS, the non-coherent intrascan integration and the interscan integration by passing the retrospective filter (RF) bank are fully combined. Secondly, the NCRVS of sea clutter has different statistics at individual radial velocity bins and the lognormal distributions are used to fit their statistics and an outlier-robust analytical bi-percentile estimator is constructed for parameter estimation. Thirdly, based on the lognormaldistributed NCRVS model, a double-channel multiscan joint detector (DC-MJD) is proposed, where the intrascan non-coherent integration channel is used to find floating or low radial velocity targets and the whitening plus intrascan non-coherent integration channel is used to find target with high radial velocity. Simulated data and measured X-band radar data are used to verify the DC-MJD detector. In comparison with the existing three multiscan detectors, the DC-MJD detector improves the detection probability by 96% in the measured data with a test target.

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Section: A Signal Model For Multiscan Joint Detectionmentioning

confidence: 99%

Section: B Multiscan Joint Detection Framework At Fast-scan Modementioning

confidence: 99%

Section: B Multiscan Joint Detection Framework At Fast-scan Modementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Small Target Detection Based on Noncoherent Radial Velocity Spectrum of High-Resolution Sea Clutter

Shi

Shui

Liang

et al. 2022

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

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show abstract

“…Yang et al [30] emphasized on employing Butterworth high-pass filter so as to detect a small (slowly moving) target. Jin et al [31] gave more preference to the Velocity Steering Vector (VSV) than the classical searching approaches to perform small slowly moving targets detection in spiky sea clutter.…”

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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Small target detection in sea clutter using dominant clutter tree based on anomaly detection framework

Guo,

Bai,

et al. 2024

Signal Processing

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Fast scan‐to‐scan integration algorithm for small target in sea clutter

Cited by 7 publications

References 3 publications

Small Target Detection Based on Noncoherent Radial Velocity Spectrum of High-Resolution Sea Clutter

Small Target Detection Based on Noncoherent Radial Velocity Spectrum of High-Resolution Sea Clutter

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

Small target detection in sea clutter using dominant clutter tree based on anomaly detection framework

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