A Neural Fuzzy Network Approach to Radar Pulse Compression

Abstract

To make good range resolution and accuracy compatible with a high detection capability while maintaining the low average transmitted power, pulse compression processing giving low-range sidelobes is necessary. The traditional algorithms such as the direct autocorrelation filter (ACF), least squares (LS) inverse filter, and linear programming (LP) filter based on three-element Barker code (B13 code) have been developed. Recently, the neural network algorithms were issued. However, the traditional algorithms cannot achieve the requirements of high signal-to-sidelobe ratio and low integrated sidelobe level (ISL), and the normal neural networks such as the backpropagation (BP) network usually produce the extra problems of low convergence speed and are sensitive to the Doppler frequency shift. To overcome these defects, a new approach using a neural fuzzy network to deal with pulse compression in a radar system is presented. Two different Barker codes are carried out by a six-layer self-constructing neural fuzzy network (SONFIN). Simulation results show that this neural fuzzy network pulse compression (NFNPC) algorithm has significant advantages in noise rejection performance, range resolution ability, and Doppler tolerance, which are superior to the traditional and BP algorithms

A Pulse Compression Waveform for Improved-Sensitivity Weather Radar Observations

“…Bucci and Urkowitz (1993) showed successful simulations indicating a lack of significant degradation using certain types of waveforms. A number of simulations using Barker codes were performed (Baden and Cohen 1990;Bucci et al 1997;Mudukutore et al 1998;Duh et al 2004), showing limited usefulness for weather radar. Around the same time, Griffiths and Vinagre (1994) presented a study involving the use of a piecewise LFM, also known as nonlinear frequency modulation (NLFM), for use with satellitebased weather radar systems.…”

Some Aspects of Sidelobe Reduction in Pulse Compression Radars Using NLFM Signal Processing

“…pure (continuous) NLFM waveforms using usually, iterative methods [11][12][13], stationary phase principle [14][15][16], Zak transform [17,18], suitable weighting/convolutional functions [19][20][21][22], explicit functions cluster algorithm [23,24] or marginal Fisher's information-based techniques [25] etc. Also, many of the above described NLFM methods are implemented by standard computational algorithms, but some interesting approaches connected with the artificial intelligence (AI) paradigms are also discussed in literature [26][27][28].…”

Development and performance evaluation of generalised Doppler compensated adaptive pulse compression algorithm

“…The multilayer artificial neural network (MLANN) with backpropagation (BP) [11, 12] and an extended Kalman filtering (EKF)‐based learning algorithm [13] have been reported for achieving efficient pulse compression. A self‐constructing neuro‐fuzzy network has been proposed as a pulse compressor [14] for the binary phase coded signals to provide improved pulse compression performance. Further, a radial basis function (RBF) network with improved convergence speed and performance compared with previously proposed neural networks has been suggested [15].…”