Missile Borne Phased Array Radar Clutter Suppression Algorithm And Angle Measurement Simulation Research

The performance of missile borne phased array radar has a direct impact on the hit rate of the missile. It needs to solve ground clutter suppression and target angle measuring two issues. This dissertation analyzes clutter characteristics and effect of dive motion on clutter spectrum when the airborne phased array radar is in forward-looking mode. As space and computing resources are limited on missile borne platform, signal processing is necessary to conduct at sub-array level taking into account of the large amount of calculation of full space time adaptive processing. In order to avoid the huge computation of eigenvalue decomposition in reduced rank processing, reduced dimension adaptive processing method is widely used. Sparse STAP has the advantages of small number of independent samples, fast convergence speed and so on. It has become a hot research direction. The research of this article mainly has the following several aspects:The signal model is established for missile borne phased array radar diving. The accurate relation expression of doppler frequency and spatial frequency both in azimuth and elevation of subduction angle are deduced. Radar clutter space-time distribution characteristics in diving motion mode are drawn. According to the actual situation of engineering, this chapter considers the effect of altitude clutters, irregular surface array sub array division, ambiguity of range and velocity due to the change of target distance and speed to approximate the real echo data in actual time.Due to small volume in missile borne platform, less resources in missile than in plane, high real-time requirements and irregular distribution of radar array, this chapter divide irregular surface array into sub-array firstly and select the appropriate reduced dimension space-time adaptive processing algorithm to meet the performance of clutter suppression and real-time requirements. This chapter introduces the classical non adaptive moving target detection algorithm and the principle of joint domain localized, one kind of reduced dimension space time adaptive processing methods. This article compares the performance difference of the two algorithms.Sparse filter space-time adaptive processing principle is introduced. The recursive least squares algorithm of sparse filter based on matrix norm 1 is derived. A lq norm sparse recovery algorithm based on cyclic descent is presented. Combined with the sidelobe canceller structure, a lq norm sparse constraint of space-time filter is added in the cost function. Design a space time adaptive processing algorithm based on lq norm and cyclic descent algorithm. The derivation and realization process is presented and apply it to missile borne radar clutter suppression scene. The simulation results show that the algorithm has faster convergence speed and higher signal-to-noise ratio under same sample numbers than previous sparse algorithms.The traditional principle of mono-pulse sum and difference beam angle measurement is derived. A novel method is proposed to effectively estimate the direction of arrival (DOA) and phase errors of uniform linear arrays (ULA) simultaneously in which only a part of the array is calibrated. An arbitrary calibrated sensor (except the reference one) and its phase error information are necessary in this algorithm. The proposed method appropriately reconstruct the data matrix and establish a series of linear equations with respect to the unknown parameters through eigenvalue decomposition (EVD). The direction of arrival (DOA) and the unknown phase errors can be determined directly by the least squares (LS) method. The simulation results show that the algorithm has lower root mean square error and better accuracy.