Research On The Application Of Sparse Reconstruction On Parameter Estimation Of Targets In Sky-wave Radar

Sky-wave over-the-horizon radar has the advantages of far detection range, wide monitoring range, cost-effective, being outstanding in anti-stealth and resisting low altitude raid. It has drawing attention of the world’s major countries and has been widely used in many kinds of military scope and civilian areas like sea state monitoring.Compared with conventional radar, the special working mode brings many advantages for sky-wave radar. However, the complex working environment which sky-wave radar owns also force it to face a series of challenges: under the condition of long coherent accumulation time, the Doppler spread of maneuvering targets will be more serious which makes the detection an parameter estimation of maneuvering targets even harder; the received signal contains radio frequency interference and transient interference with strong directivity, these powerful interference are important factors that limit detection and parameter estimation of targetsAiming at the above problems, parameter estimation of maneuvering targets and estimation of direction of arrival are researched in this thesis. The main work of this thesis is summarized as follows:（1） Comparative analysis of parameter estimation algorithms of maneuvering targetsThe Doppler spread of maneuvering targets in sky-wave radar is analyzed; two kinds of traditional algorithms of parameter estimation of maneuvering targets（parameter estimation of maneuvering targets based on high-order ambiguity function and parameter estimation of maneuvering targets based on high-order ambiguity function Wigner-Hough transform） are given. The measured data which contains echo of a maneuvering target is processed by these two algorithms. The performances of the two algorithms are analyzed through the simulation experiments.（2） Research on parameter estimation algorithm based on sparse reconstructionIn view of the shortcomings of the existing algorithms, a new parameter estimation algorithm based on sparse reconstruction is given in this thesis. This algorithm formulates the inverse short time Fourier transform into a sparse optimization problem, then solve the problem by split bregman iteration, the motion parameters was estimated by taking the Hough transform of the sparse time-frequency distribution. Simulation results show, the precision of the given algorithm is higher than the HAF algorithm and the WHT algorithm, and the required signal-to-noise ratio of the given algorithm is lower.（3） Research on DOA estimation algorithm based on sparse reconstructionA DOA estimation algorithm based on Meridian prior is given. The Meridian prior, which has a probability density focused on zero, was imposed on the sparse vector. The maximum a posteriori probability estimation was solved to obtain the estimation of the direction of arrival. Simulation results show, compared with the classic l₁-SVD algorithm, the given algorithm has better resolution performance.