Scale‐space theory‐based multi‐scale features for aircraft classification using HRRP

Liu, Jia; Fang, Ning; Xie, Yong; Wang, Bao Fa

doi:10.1049/el.2015.3583

Cited by 32 publications

(24 citation statements)

References 9 publications

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“…Before network training, data pre-processing is needed to solve the amplitude-scale and time-shift sensitivities. According to the previous study [ 3 , 5 , 6 , 7 ], we usually use the energy normalization method and time-shift compensation algorithm to cope with the above issues.…”

Section: Resultsmentioning

confidence: 99%

“…The illustration of an HRRP sample from a plane target is shown in Figure 1 . Since HRRP contains the target-important structural features such as target size and the distribution of scattering centers, etc., radar HRRP target recognition has drawn much attention from the radar automatic target recognition community [ 3 , 4 , 5 , 6 , 7 ].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Radar target recognition based on high-resolution range profile (HRRP) has become a research hotspot due to the acquisition and processing of HRRP data being relatively easy [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ]. However, the non-cooperative recognition [ 8 , 9 ] with limited training samples is a challenging task.…”

Section: Introductionmentioning

confidence: 99%

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Radar HRRP Target Recognition Based on Stacked Autoencoder and Extreme Learning Machine

Zhao

Liu

Huang

et al. 2018

Sensors

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A novel radar high-resolution range profile (HRRP) target recognition method based on a stacked autoencoder (SAE) and extreme learning machine (ELM) is presented in this paper. As a key component of deep structure, the SAE does not only learn features by making use of data, it also obtains feature expressions at different levels of data. However, with the deep structure, it is hard to achieve good generalization performance with a fast learning speed. ELM, as a new learning algorithm for single hidden layer feedforward neural networks (SLFNs), has attracted great interest from various fields for its fast learning speed and good generalization performance. However, ELM needs more hidden nodes than conventional tuning-based learning algorithms due to the random set of input weights and hidden biases. In addition, the existing ELM methods cannot utilize the class information of targets well. To solve this problem, a regularized ELM method based on the class information of the target is proposed. In this paper, SAE and the regularized ELM are combined to make full use of their advantages and make up for each of their shortcomings. The effectiveness of the proposed method is demonstrated by experiments with measured radar HRRP data. The experimental results show that the proposed method can achieve good performance in the two aspects of real-time and accuracy, especially when only a few training samples are available.

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Section: Resultsmentioning

confidence: 99%

Section: Theoretical Backgroundmentioning

confidence: 99%

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Radar HRRP Target Recognition Based on Stacked Autoencoder and Extreme Learning Machine

Zhao

Liu

Huang

et al. 2018

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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“…Obtaining the high-resolution range (HRR) profile (HRRP) of targets is a significant capacity of modern radars and the HRRP plays a critical role in synthetic aperture radar [ 1 ], automatic target recognition [ 2 , 3 , 4 , 5 ] and classification [ 6 , 7 ], and adaptive waveform design [ 8 , 9 ], etc. It has been widely acknowledged that the HRR profiling performance is associated with the waveform utilized [ 10 ], and improper waveforms can result in large profiling errors [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Radar Constant-Modulus Waveform Optimization for High-Resolution Range Profiling of Stationary Targets

Yue

Xin

et al. 2017

Sensors

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The high-resolution range (HRR) profile is an important target signature in many applications (e.g., automatic target recognition), and the radar HRR profiling performance is highly dependent on radar transmitted waveforms. In this paper, we consider the constant-modulus (CM) waveform optimization problem to improve HRR profiling performance for stationary targets. Firstly, several fundamental bounds regarding the profiling ambiguity, stability, and accuracy are derived. Further investigation reveals that the stability and accuracy of HRR profiling are unified in the white noise case. Aimed at improving the profiling stability and accuracy, we design two types of CM radar waveforms—the arbitrary-phase and QPSK waveforms—through a customized Gaussian randomization method. The performance of LFM waveforms is also discussed. Numerical experiments show that the optimized CM waveforms can dramatically enhance the profiling performance over the unoptimized ones.

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Scale‐space theory‐based multi‐scale features for aircraft classification using HRRP

Cited by 32 publications

References 9 publications

Radar HRRP Target Recognition Based on Stacked Autoencoder and Extreme Learning Machine

Radar HRRP Target Recognition Based on Stacked Autoencoder and Extreme Learning Machine

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

Radar Constant-Modulus Waveform Optimization for High-Resolution Range Profiling of Stationary Targets

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