Computational Burden Resulting from Image Recognition of High Resolution Radar Sensors

López-Rodríguez, Patricia; Fernández-Recio, R.; Bravo, Ignácio; Gardel, Alfredo; Lázaro, José; Rufo, Elena

doi:10.3390/s130405381

Cited by 10 publications

(8 citation statements)

References 35 publications

(48 reference statements)

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“…According to that, in this paper identification of HRRPs coming from data of real in-flight targets is carried out by comparison with a database of simulated/synthetic HRRPs. This approach is barely applied in the open literature [ 2 , 28 , 29 ] but it is a very interesting field due to the ease in the database population and the fast evaluation of algorithms. The main drawback found, as noted, is that predictions have a very clean signature while actual HRRPs suffer from noise and other unwanted effects, making the recognition process similar to a real situation where collected profiles could be noisier than those in the database.…”

Section: Methodsmentioning

confidence: 99%

Non-Cooperative Target Recognition by Means of Singular Value Decomposition Applied to Radar High Resolution Range Profiles

López-Rodríguez¹,

Escot-Bocanegra²,

Fernández-Recio

et al. 2014

Sensors

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Radar high resolution range profiles are widely used among the target recognition community for the detection and identification of flying targets. In this paper, singular value decomposition is applied to extract the relevant information and to model each aircraft as a subspace. The identification algorithm is based on angle between subspaces and takes place in a transformed domain. In order to have a wide database of radar signatures and evaluate the performance, simulated range profiles are used as the recognition database while the test samples comprise data of actual range profiles collected in a measurement campaign. Thanks to the modeling of aircraft as subspaces only the valuable information of each target is used in the recognition process. Thus, one of the main advantages of using singular value decomposition, is that it helps to overcome the notable dissimilarities found in the shape and signal-to-noise ratio between actual and simulated profiles due to their difference in nature. Despite these differences, the recognition rates obtained with the algorithm are quite promising.

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Section: Methodsmentioning

confidence: 99%

Non-Cooperative Target Recognition by Means of Singular Value Decomposition Applied to Radar High Resolution Range Profiles

López-Rodríguez¹,

Escot-Bocanegra²,

Fernández-Recio

et al. 2014

Sensors

Self Cite

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“…In [69], a methodology is presented for automatic target recognition, based on Inverse Synthetic Aperture Radar (ISAR) with the generation of high-resolution imagery.…”

Section: Applications Based On Sensorsmentioning

confidence: 99%

Sensors and Technologies in Spain: State-of-the-Art

Pajares

2014

Sensors

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“…(2) methods based on high-resolution range profile (HRRP) [7][8][9][10][11]. Liu et al [12] proposed a multi-scale target classification method based on the scale-space theory through extracting features from HRRP; and (3) methods based on inverse synthetic aperture radar (ISAR) [13][14][15][16][17]. A shape extraction based aircraft target classification method using ISAR images is proposed in [18].…”

Section: Introductionmentioning

confidence: 99%

Aircraft Target Classification for Conventional Narrow-Band Radar with Multi-Wave Gates Sparse Echo Data

Wang¹,

Tang²,

Chen³

et al. 2019

Remote Sensing

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For a conventional narrow-band radar system, the detectable information of the target is limited, and it is difficult for the radar to accurately identify the target type. In particular, the classification probability will further decrease when part of the echo data is missed. By extracting the target features in time and frequency domains from multi-wave gates sparse echo data, this paper presents a classification algorithm in conventional narrow-band radar to identify three different types of aircraft target, i.e., helicopter, propeller and jet. Firstly, the classical sparse reconstruction algorithm is utilized to reconstruct the target frequency spectrum with single-wave gate sparse echo data. Then, the micro-Doppler effect caused by rotating parts of different targets is analyzed, and the micro-Doppler based features, such as amplitude deviation coefficient, time domain waveform entropy and frequency domain waveform entropy, are extracted from reconstructed echo data to identify targets. Thirdly, the target features extracted from multi-wave gates reconstructed echo data are weighted and fused to improve the accuracy of classification. Finally, the fused feature vectors are fed into a support vector machine (SVM) model for classification. By contrast with the conventional algorithm of aircraft target classification, the proposed algorithm can effectively process sparse echo data and achieve higher classification probability via weighted features fusion of multi-wave gates echo data. The experiments on synthetic data are carried out to validate the effectiveness of the proposed algorithm.

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Computational Burden Resulting from Image Recognition of High Resolution Radar Sensors

Cited by 10 publications

References 35 publications

Non-Cooperative Target Recognition by Means of Singular Value Decomposition Applied to Radar High Resolution Range Profiles

Non-Cooperative Target Recognition by Means of Singular Value Decomposition Applied to Radar High Resolution Range Profiles

Sensors and Technologies in Spain: State-of-the-Art

Aircraft Target Classification for Conventional Narrow-Band Radar with Multi-Wave Gates Sparse Echo Data

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