Performance Enhancement of Target Recognition Using Feature Vector Fusion of Monostatic and Bistatic Radar

Abstract

Abstract-This paper proposes a fusion technique of feature vectors that improves the performance of radar target recognition. The proposed method utilizes more information than simple monostatic or bistatic (single receiver) algorithms by combining extracted feature vectors from multiple (two or three) receivers. In order to verify the performance of the proposed method, we use the calculated monostatic and bistatic RCS of three full-scale aircraft and the measured monotatic and bistatic RCS of four scalemodel targets. The scattering centers are extracted using one-dimensional FFT-based CLEAN and then used as feature vectors for a neural network classifier. The results show that our method has better performance than algorithms that solely use monostatic or bistatic data.

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.…”

“…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.…”

“…Due to the higher phase sensitivity than the amplitude's in target-aspect [5], a trigonometric function couple (TFC), i.e., the cosine and sine functions, is employed to reduce its sensitivity and improve the recognition stability. Consider that the fusion recognition techniques always appear in RATR [14,15], two fusion recognition strategies are also proposed in this paper, of which the first strategy is specially designed for phase recognition while the second is based on HRRP's phases and amplitudes. Additionally, in order to clearly analyze the two proposed strategies, linear discriminant analysis (LDA) and 1-nearest neighbor (1-NN) ruler are both employed in them to demonstrate the detailed processes [19,20].…”

A Study on the Performance Enhancement of Radar Target Classification Using the Two-Level Feature Vector Fusion Method

“…The target classification experiment was then per-formed by fusing these feature vectors. Other fusion techniques are presented in [6,7].…”