Distributed Radar Clutter And Interference Suppression Technology For Moving Platforms

Due to the limitation of factors such as aircraft load and power supply,the detection power of a single airborne radar will reach a bottleneck and it is difficult to continuously increase.Distributed radar adopts an open architecture,which can continuously increase the antenna area and transmission power by increasing the number of drones,which is an effective way to improve the detection power of space-based detection systems.When the UAVs are relatively close together,all antennas and transceiver systems constitute a sparse array antenna radar.The super-large aperture provides potential for improving the radar's target detection,parameter estimation and resolution performance.Of course,while the distributed system brings advantages,it also brings some new problems.Like traditional airborne radars,airborne distributed radars are also affected by ground clutter and interference when working,and the clutter and interference on the battlefield in the future will be more complex,and signal processing will be more difficult.Therefore,in view of the clutter and interference suppression problems in the UAV distributed radar target detection,this paper conducts signal processing research.Aiming at the clutter suppression problem of airborne distributed radar,a space-time adaptive processing method of airborne distributed array dimensionality reduction is proposed,which significantly reduces the computational complexity and sample requirements of space-time adaptive processing.Aiming at the problem of simultaneous suppression of clutter and interference in airborne distributed radars in a complex interference environment,a STAP method constructed with a small sample covariance matrix is proposed,which makes full use of the entire system without increasing the number of training samples.The degree of freedom in the airspace improves the ability to combat dense interference environments.The main work of the thesis is summarized as follows:1.The working mode and signal model of the airborne distributed radar coherent transmission and coherent reception are studied.It is pointed out that in the search state,the distributed radar can use the sub-radars to transmit in turn,and the working mode of multi-beam coherent reception at the receiving end at the same time,to make full use of the transmitted energy while avoiding the formation of grating lobes.A signal model of the airborne distributed radar is established for this working mode.2.The clutter suppression method of airborne distributed radar is studied.From the perspective of the likelihood ratio test,the clutter decoherence and target decoherence caused by the sparse distribution of the antenna space are divided into three cases,and the characteristics of the clutter covariance matrix are analyzed for each decoherence situation.And give the corresponding adaptive processing method.Aiming at the situation that the clutter is decohered and the target is not decohered,a space-time adaptive processing method of airborne distributed array dimensionality reduction is proposed,which significantly reduces the computational complexity and sample requirements of the space-time adaptive processing.Finally,the influence of front angle error and velocity angle error on the performance of the method is analyzed,and the corresponding compensation method is given.3.The method of simultaneously suppressing clutter and interference of airborne distributed radar is studied.Full array STAP can effectively suppress more interference,but there is a problem of insufficient training samples when suppressing clutter.In response to this situation,a STAP method of small sample covariance matrix construction is proposed.This method uses the different structural characteristics of the clutter covariance matrix and the interference covariance matrix,and constructs the clutter and interference covariance matrix through separate estimation and block diagonal continuation methods,and then synthesizes the complete covariance matrix to improve Improve the sample utilization efficiency.