Orthogonal kernel projecting plane for radar HRRP recognition

Complex HRRP Target Recognition Based on Phase and Amplitude Fusion Analysis

“…Due to the massive recognition researches focusing on real HRRPs during past years [10][11][12][13][14][15][16], the discriminant process of HRRP's amplitudes is moderately reduced as that. Firstly, through the preprocessed method offered in [2], we obtain the training spaceĀ from the original space A. Secondly, with the help of LDA, we obtain the FPS U A from the training spaceĀ.…”

“…The original HRRP data used to evaluate the recognition performance was measured by a C-band ISAR radar with bandwidth 400 MHz [4][5][6][7][10][11][12][13][14], including the 1 st , 2 nd , 4 th and 7 th segments of An-16, the 1 st , 2 nd , 4 th and 7 th segments of Jiang (Cessna Citation S/II), and the 1 st , 2 nd , 4 th and 5 th segments of Yak-42, with each segment containing 260 complex HRRPs. In order to evaluate the proposed strategies, two experimental datasets are designed as follows.…”

“…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.…”

“…These statistical discriminant methods were generally employed to deal with HRRP's amplitudes, that is to say, their prevalent applications were designed to extract the discrimination information only from real HRRPs rather than the complex ones [2,[10][11][12][13][14][15][16], thereby probably losing some valuable discriminant information contained in the phases and potentially resulting in a dissatisfied recognition performance sometimes [17]. Nevertheless, in addition to these traditional applications, there has been little recognition work done via directly using the phase information of complex HRRPs due to two main reasons.…”

Ship Recognition Based on HRRP via Multi-Scale Sparse Preserving Method

“…Hence, the kernel trick is investigated to map the nonlinear and inseparable data into a high dimensional feature space, in which data is easily grouped together and is linearly separable. Inspired by the fact above, Liu et al [15] introduced a kernel joint discriminant analysis (KJDA) method which utilizes more potential information captured from global and local criterion of input data in kernel feature space. Zhou et al [16] proposed an orthogonal kernel projecting plane (OKPP) algorithm for radar target recognition.…”