| Radar is one of the main ways to obtain information in the electromagnetic environment,making it the primary target of interference in electronic warfare.Airborne radars installed on floating platforms have a much longer line of sight than other radars and are more vulnerable to attack.Modern radar interference sources are becoming more complicated due to the advancement of radar.Among them,active interference is a larger problem that airborne radar faces in complex electromagnetic environments.Most of the current radars do not have interference recognition capabilities.It is impossible to select interference suppression measures for the time-varying electromagnetic interference environment of the battlefield.The radar sensing capability needs to be improved.Zero-stretch stretching technology is an effective method to suppress non-stationary interference caused by airborne platform jitter,interference source motion,etc.,but the traditional stretching technology has the problem of poor adaptive capability.In particular,when confronting large deviation angle interference in a short time,the radar needs to increase the update rate of the weight vector or widen the nulling area in exchange for a high signal-to-noise ratio output.In addition,the cancellation algorithm is only suitable for side-lobe interference.When the radar is subject to main-lobe interference,such as high-power follow-up interference,this type of algorithm fails.Therefore,a more robust method for suppressing main-lobe interference needs to be studied.First of all,this paper does some research on interference perception algorithms.Step 1: Starting from the interference mechanism of active interference,the common interference is divided into three categories: radio frequency noise interference,dense false target interference,and compound interference.Step 2: Extract the interference signal in time,frequency,and time-frequency domains.With the same characteristic parameters,the variation curve of the characteristic parameters with the interference-to-noise ratio is obtained by simulation.The third part is to classify the interference signals using support vector machines.The results show that the method used in this paper can effectively distinguish the three types of interference signals mentioned above,and it is of great significance to improve the cognitive ability of radar.Secondly,in order to overcome the shortcomings of the existing zero-stretching and widening algorithms in the presence of strong directivity and large deviation angles,the depth of the nulling becomes shallower and the interference suppression performance is severely reduced.Adapt to the zero trap optimization design algorithm.The algorithm simultaneously tapers the self-covariance matrix of the auxiliary channel and the crosscovariance matrix of the main and auxiliary channels to achieve adaptive control of the nulled region.The tapered matrix does not need to interfere with the information.Computing resources can be generated offline.Simulation results show that the method can achieve adaptive widening of the zero-sag region and improve the robustness of non-stationary interference suppression.Finally,in view of the main lobe interference,high-power suppression interference will seriously affect the radar detection performance.This paper proposes a new method of target space parameter estimation under the main lobe interference,which realizes the DOA estimation of the signal.The first step: whitening the received multi-channel signals;the second step: using a fast fixed-point independent component analysis algorithm to separate the sub-arrays at the same time;the third step: selecting the target signal separation channels and comparing the phase difference between the sub-arrays to extract the Angular information.Simulation results show that the method has good performance in extracting target spatial parameters in strong main lobe interference. |
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