Efficient Radar Target Recognition Using a Combination of Range Profile and Time-Frequency Analysis

Han, Seung-Ku; Kim, Dong-Won; Park, Sang-Hong; Kim, and Kyung-Tae

doi:10.2528/pier10071601

Cited by 45 publications

(35 citation statements)

References 18 publications

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“…As a key application of SAR, automatic target recognition (ATR) based on SAR has a great value both in military and commercial applications. However, how to interpret the SAR images and recognize the true targets correctly still remains to be studied and explored [6][7][8][9][10][11][12][13][14][15][16]. Currently, the proposed recognition methods for SAR targets are mainly template-based matching strategy [17] and model-based reconstruction method [18], together with the kernelbased method [19] which has been adopted for a wide range of applications because of its excellent performance.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Scale Local Phase Quantization Plus Biomimetic Pattern Recognition Method for Sar Automatic Target Recognition

Zhai¹,

Li²,

Gan³

et al. 2013

PIER

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Abstract-Synthetic aperture radar (SAR) automatic target recognition (ATR) has been receiving more and more attention in the past two decades. But the problem of how to overcome SAR target ambiguities and azimuth angle variations has still left unsolved. In this paper, a multi-scale local phase quantization plus biomimetic pattern recognition (BPR) method is presented to solve these two difficulties. By applying multiple scales local phase quantization (LPQ) on the observed SAR images, the blur and azimuth invariant features can be extracted, and these features are fusion at consecutive multiple scales to achieve better results. Then PCA method is applied to further reduce the feature dimension and achieve its efficiency. Finally, high dimensional space geometry covering method based on BPR theory is adopted to construct hyper sausage neuron links for target recognition. Experiments on the MSTAR database show that the proposed method can achieve satisfying recognition accuracy compared with other stateof-the-art methods.

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Section: Introductionmentioning

confidence: 99%

A Multi-Scale Local Phase Quantization Plus Biomimetic Pattern Recognition Method for Sar Automatic Target Recognition

Zhai¹,

Li²,

Gan³

et al. 2013

PIER

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“…Hence, it is needed to develop efficient features and classifiers that can discriminate automobiles under low-resolution condition. To date, most researches have focused on the development of the classifier [21,22]. However, high classification ratios can be achieved by using a simple classifier once the features of each automobile are properly separated in the feature space [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Efficient Classification of Low-Resolution Range Profiles of Automobiles Using a Combination of Useful Features

Jung¹,

Park²

2013

PIER

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Abstract-The range profile (RP) of an automobile is derived by compressing the wideband radar signal, and it can be utilized for the classification and thus contribute to lane change and collision avoidance. However, the limited radar bandwidth due to the cost and the system complexity impedes the successful classification. This paper proposes an efficient method to construct an efficient feature vector of the automobile RP through combined use of the central moment, the information on the maximum-minimum and the peak information. Simulation results using the five automobile models composed of point scatterers and a simple nearest neighbor classifier prove that the proposed method improves the classification result, especially in the multi-aspect classification.

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“…Among several kinds of the windband radar target signatures, such as 2-D and 3-D radar target images [3], HRRP is a promising signature and more easy to be acquired in actual application, but it is highly sensitive to target-aspect, time-shift and amplitude-scale variations [4][5][6], so how to extract robust and effective feature from the raw signal becomes a key problem in HRRP-based radar automatic target recognition (RATR). During the past decade, many measured and simulated experimental results also confirmed that some physical structure information naturally contained in complex HRRPs, such as target size [7], scatterer distribution [8,9], amplitude fluctuation [10][11][12][13][14][15][16], is very beneficial to HRRP-based RATR, and accordingly, a number of statistical methods have been proposed for feature extraction and dimension reduction.…”

Section: Introductionmentioning

confidence: 99%

Complex HRRP Target Recognition Based on Phase and Amplitude Fusion Analysis

Fu¹,

Zu²,

Qiao³

et al. 2014

PIER M

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Abstract-Due to the traditional recognition researches prevalently focusing on HRRP's amplitudes while almost completely neglecting the phases, this paper attempts to directly prove the discriminant availability of HRRP's phases via two proposed fusion recognition strategies. The first strategy includes three sub-processes, respectively, based on phase cosine, phase sine and their fusion. The second strategy also includes three sub-processes, respectively, based on phases, amplitudes and their fusion. Additionally, a trigonometric function couple (TFC) method is used to reduce the phase sensitivity. Several measured experimental results indicate as follows. Firstly, employing TFC can perform much better. Secondly, the two fusion recognition sub-processes apparently outperform the corresponding sub-processes constructing them. Finally, phase information usually has a better noise immunity than amplitude information, and fusing phase information into amplitudes may improve the traditional recognition performance. Therefore, the availabilities of HRRP's phases and the two fusion strategies have been experimentally proven.

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Efficient Radar Target Recognition Using a Combination of Range Profile and Time-Frequency Analysis

Cited by 45 publications

References 18 publications

A Multi-Scale Local Phase Quantization Plus Biomimetic Pattern Recognition Method for Sar Automatic Target Recognition

A Multi-Scale Local Phase Quantization Plus Biomimetic Pattern Recognition Method for Sar Automatic Target Recognition

Efficient Classification of Low-Resolution Range Profiles of Automobiles Using a Combination of Useful Features

Complex HRRP Target Recognition Based on Phase and Amplitude Fusion Analysis

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