Parameter Estimation Of Frequency Hopping Signals Based On Time Frequency Analysis

Frequency Hopping (FH) is one of the most commonly used way about spread spectrum communication technique which develops fast in military field due to its good immunity against interferences, low probability of intercept and facility in communication networking. Along with the development of FH and each kind of hardware technology, the FH communications turn toward high hop rate, wide hop frequency band and more FH points. Farther, FH technology is unified with other spread spectrum ones gradually and uses the auto-adapted FH working which enhanced the ability of military equipments against jamming and interception enormously and brought up a new austere challenge for communication countermeasures, especially under crowed signal environment in the modern electronic warfare.As a novel signal processing method, time-frequency (TF) analysis may overcome the drawback of Fourier Transform about nonstationary signal process, which could depict the frequency change law of signal versus time using the two-dimensional function of time and frequency. In recent years, it has made the significant development in the fundamental research aspect, and obtained the widespread application in reality. Obtaining TF representation of the non-cooperation FH signal embedded in noise and its parameter such as hopping during, hop timing and hopping frequency is precondition to achieve the interception enemy side correspondence, produce the best jamming signal and disintegrate the enemy side normal correspondence finally, therefore becomes in modern military correspondence countermeasure research key one. In the present paper, the problems about kernel function design and entropy measure appraisal to enhance the TF concentration and cross-term suppression of TF distribution of FH signal, and atomic decomposition based on particle swarm optimization are discussed. At last, this thesis has engaged in extensive research on parameter estimation algorithm about FH signal.In this thesis, the FH signal ambiguity function formula has been deduced firstly, which the auto-components are centered the origin of the ambiguity plane and the cross-components are located away from it, and derived one kernel function in sampling function shape using the relations of Wigner distribution and ambiguity function of FH signal. The corresponding TF distribution could suppress the cross-term interference effectively, preserve as far as possible many from auto-component energy since the designed kernel function realized the possible match with the auto-component of FH signal, thus gained the better parameter estimation performance.The Cohen TF distributions are not only numerous, but also contains one or more parameters. The choice of kernel function type and parameter are important for the performance of TF distribution. It is major problem in TF analysis of FH signal to evaluate the performances of different distributions, select better type and parameters of TF distribution. In this thesis, we analysis the results of entropy measure in TF distribution performance evaluation and propose a novel optimization method about kernel parameters based on Renyi entropy with normalization volume. The parameter estimation variance is reduced effectively and estimation precision is increased based on Renyi entropy TF distribution, combining parameter optimization by sequential quadratic programming method.The TF analysis method is mainly divided into atom decomposition and the energy distribution according as the realization way. The former is to represent the signal as the weighted sun of TF elementary functions named atoms in order to eliminate the interference of cross-terms at all. In this thesis, we introduce the particle swarm optimization (PSO) algorithm to overcome the bottleneck of huge computation burden and design a novel parameter estimation algorithm of FH signal after obtaining atom parameters which match the FH signal components. This algorithm get rid of the Signal to Noise Ratio (SNR) threshold value effect of parameter estimation based on time-frequency plane and could obtain good estimate results under the low SNR condition.The PSO algorithm based on matching pursuit can only select one atom which matches the single component of FH signal in each iterate, thus the efficiency is reduced. In this thesis, we design a new PSO algorithm based on modified multi-species procedure which uses the algorithm convergence in the search procedure of the particles using multimodal characteractic of the inner product of FH signal and the atoms of the dictionary. Computer simulation results show that this algorithm could give all the TF atoms that match the components about FH signal and could enhance the parameter estimation performance under low SNR which is suitable for many kinds of FH signal.