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Fast Parameter Estimation of Wideband LFM Signals Based on MP Decomposition and Array Error Correction

Author: FangLiLi
Tutor: WangJianYing
School: Southwest Jiaotong University
Course: Signal and Information Processing
Keywords: Broadband linear FM signal Frequency estimation Genetic Algorithms Particle swarm optimization Error correction
CLC: TN911.7
Type: Master's thesis
Year: 2008
Downloads: 147
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In signal analysis and signal processing, the following three important research significance: (1) the representation of the signal and decomposition method; (2) signal decomposition algorithm; (3) signal in signal processing applications. Sparse representation of the signal sparse decomposition, the signal of a new, very simple representation and decomposition way, has great research value and broad application prospects. Signal sparse decomposition of computational complexity is very high, and the key factors that hinder its development. To promote signal sparse, sparse decomposition, its fast algorithm is very necessary. Otherwise, the signal sparse decomposition and signal sparse representation will not be practical, but can only stay at the research stage. Genetic algorithm and particle swarm optimization algorithm is applied to the field of broadband linear FM signal parameter estimation, respectively, A direct search and particle swarm optimization hybrid optimization (DS-PSO) algorithm fast estimation of the frequency of the signal, and A mixed genetic algorithm and particle swarm optimization (GA-PSO) algorithm for rapid estimation of signal frequency, the two rapid frequency estimation algorithm and angle sub-block search algorithm combined with the estimated signal DOA, greatly improves on the line FM signal parameter estimation speed. The presence of the array error signal parameter estimation performance degradation. In this paper, particle swarm optimization algorithm is applied to the field of array calibration A particle swarm optimization algorithm estimate uniform linear array amplitude and phase errors. Main work and contributions: 1. Introduced in recent years, the new signal representation theory of thinking - signal sparse representation and sparse decomposition, focusing on the specific process sparse decomposition of the most commonly used method - matching pursuit (MP) algorithm. 2. Frequency domain analysis of the chirp signal characteristics, respectively, given the frequency parameter estimation and DOA estimation algorithm based on MP decomposition. Description atoms library composition and MP decomposition process, and gives the algorithm steps summarize. MP algorithm is given by a large number of experimental simulation performance analysis. 3 describes in some detail the basic principles of genetic algorithms and particle swarm optimization, focusing on two hybrid optimization algorithm strategy for proposed separately based on the the MP decomposition frequency parameters and DOA estimation algorithm in signal decomposition computational problems A direct search and particle swarm optimization hybrid optimization algorithm to quickly estimate the frequency of the signal (DS-PSO), and a mixed use of genetic algorithm and particle swarm optimization (GA-PSO) algorithm for fast signal frequency estimated detailed introduction to the principles of the fast algorithm, and gives the algorithm steps summarize. Two rapid frequency estimation algorithm and angle block search algorithm combined with the estimated signal DOA, greatly improve the estimated speed of the chirp signal parameters. Finally, through a large number of experimental simulation speed fast algorithm analysis and comparison, to prove the effectiveness of the algorithm. Study array of different types of error of several existing error correction method, error estimate for the uniform linear array in amplitude due to the special structure of the array result in part of the error of this issue can not be accurately estimated chapter presents a particle swarm algorithm uniform linear array, amplitude and phase errors of estimation method, a more accurate estimate of the amplitude and phase errors by correcting the steering vector, you can get an accurate direction of arrival of the source. The simulation results proved the effectiveness and feasibility of the algorithm.

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