Ionospheric Clutter Suppression in HF Surface Wave Radar

Wu, Meng; Wen, Biyang; Zhou, Hao

doi:10.1163/156939309789108570

Cited by 26 publications

(13 citation statements)

References 6 publications

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“…Although this study chooses only simple models of ships as targets of recognition, one can add different types of random components to RCS for modeling RCS of complex targets. This study can be applied to many other applications in radar target recognition [7][8][9][10][11][12][13][14][15][16][17][18][19].…”

Section: Resultsmentioning

confidence: 99%

Application of Ica Technique to Pca Based Radar Target Recognition

Huang¹,

Lee²

2010

PIER

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Abstract-In this paper, the ICA (independent component analysis) technique is applied to PCA (principal component analysis) based radar target recognition. The goal is to identify the similarity between the unknown and known targets. The RCS (radar cross section) signals are collected and then processed to serve as the features for target recognition. Initially, the RCS data from targets are collected by angular-diversity technique, i.e., are observed in directions of different elevation and azimuth angles. These RCS data are first processed by the PCA technique to reduce noise, and then further processed by the ICA technique for reliable discrimination. Finally, the identification of targets will be performed by comparing features in the ICA space. The noise effects are also taken into consideration in this study. Simulation results show that the recognition scheme with ICA processing has better ability to discriminate features and to tolerate noises than those without ICA processing. The ICA technique is inherently an approach of high-order statistics and can extract much important information about radar target recognition. This property will make the proposed recognition scheme accurate and reliable. This study will be helpful to many applications of radar target recognition.

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Section: Resultsmentioning

confidence: 99%

Application of Ica Technique to Pca Based Radar Target Recognition

Huang¹,

Lee²

2010

PIER

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“…According to [21,22], the NBRFI ought to have a strong rangedomain correlation at all range cells, but the WBRFI ought to have a strong range-domain Journal of Electromagnetic Waves and Applications 449 correlation only at the adjacent range cells, and the range-domain correlation decays sharply as the range cell interval increasing. Now, we analyze the range-domain correlation of the RFI with some typical real data collected by a HFSWR in Hainan Province, which was developed by Wuhan University of China [24]. Our HFSWR adopts the FMICW (frequency modulated interrupting continuous wave) waveform.…”

Section: Range-domain Correlationmentioning

confidence: 99%

Radio frequency interference suppression in high frequency surface wave radar based on range–domain correlation

Pan

Zhou

Wen

2012

Journal of Electromagnetic Waves and Applications

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High frequency surface wave radar (HFSWR) is often subject to the serious impact of external radio frequency interference (RFI), which must be suppressed efficiently in order to improve the viability of the radar. In this paper, we consider two different types of RFIs. One has a strong range-domain correlation at all range cells, while the other has a strong range-domain correlation only at the adjacent range cells. According to the difference on range-domain correlation between these two types of RFIs, we propose two different suppression methods based on the range-domain adaptive filtering. One is the far range cells method which uses the signals at the far range cells as the secondary data, and the other is the adjacent range cells method which uses the signals at the adjacent range cells as the secondary data. In the practical processing, we should select the far range cells method when the RFI has a strong range-domain correlation at all range cells. Otherwise, we should select the adjacent range cells method. Processing results of real data are presented to demonstrate the effectiveness of these two methods.

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“…In fact, HF radar signals propagate through the ionosphere in three ways: the first is near-vertical reflection; the second is to emit a wave at an angle less than vertical, reflected by the ocean or the target, and then back along the same path or sea surface; the third is back-scattering phenomenon caused by the irregularities and the fluctuations of the ionosphere [2]. Traditionally, in HFSWR, ionospheric echo is generally considered as clutter, and there have been many studies on ionospheric clutter suppression [2,3]. However, in the second scenario, ionospheric echo may carry the information of the target (such as a plane, island, or vessel).…”

Section: Introductionmentioning

confidence: 99%

Modified Multi-Mode Target Tracker for High-Frequency Surface Wave Radar

2018

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Abstract:In high-frequency surface wave radar (HFSWR), part of the radiation signal inevitably propagates upward and illuminates the target through the ionosphere due to the poor controllability of the antenna's vertical pattern. As a result, a target may have several echoes from different propagation modes, which affect target detection and tracking and is usually unmanageable in existing HFSWR systems. Without the information of the elevation angle, it is difficult to distinguish the propagation mode of measurements during the target detection phase. This paper makes the first attempt to propose a multi-mode target tracker for HFSWR to solve this problem during the target tracking phase. The multipath probability data association (MPDA) tracker is capable of exploiting multipath target signatures of discrete propagation modes and has been widely used. Based on this, we construct a modified multi-mode probability data association tracker for HFSWR to suppress false tracks caused by multiple propagation modes. Numerical simulations demonstrate that this novel tracker can effectively and accurately track the target from the measurements under multiple propagation modes in HFSWR. The processing results of the actual data collected in Weihai, China indicate that this tracker is of great significance for practical applications.

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Ionospheric Clutter Suppression in HF Surface Wave Radar

Cited by 26 publications

References 6 publications

Application of Ica Technique to Pca Based Radar Target Recognition

Application of Ica Technique to Pca Based Radar Target Recognition

Radio frequency interference suppression in high frequency surface wave radar based on range–domain correlation

Modified Multi-Mode Target Tracker for High-Frequency Surface Wave Radar

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