Clutter Mitigation And Target Tracking For Through-wall Radar

In the past decades,through-the-wall radar(TWR)has attracted more and more research interests due to its ability to perceive targets information inside closed structures.The purposes of TWR include determining the layout of the building,identifying the types of target activities within the building,and also detecting,identifying,classifying,and tracking moving targets.In the TWR system,the mitigating of clutter interference caused by wall reflection is also known as the clutter reduction.Clutter reduction is an important preprocess step because it removes the interferences and provides a reliable guarantee for the detection of clean target returns.In this thesis,two important aspects of TWR system are discussed including the clutter reduction and targets tracking.Based on the properties of low-rank characteristic of the wall reflection and sparsity of the target returns,an optimization model using the low-rank and sparse framework is proposed to solve the problem of clutter reduction.To solve the optimization problem efficiently,the bilinear factorization is adopted to simplify the nuclear norm minimization.In addition,the alternating direction method of multiplier approach is developed to derive the closed-form solution of the optimization problem.The simulations show that after clutter mitigation,the TWR system has a better detection performance with the radar antenna positions N?51.Furthermore,the proposed method handles the scenario of the antenna array and the wall not being parallel by a use of weighted nuclear norm minimization.The experimental results demonstrate that the proposed method is very effective at clutter mitigation in different scenes.Compared with existing clutter mitigation algorithms,the proposed method has clearer imaging results.Finally,in the case of different transmitted waveforms,the performance of TWR system using the step-frequency synthesized signal is superior to other detection waveforms.For the target-tracking task,after the clutter reduction,a target tracking strategy within observation windows is proposed.First,the target detection and locations in a series of observation windows are generated.Second,the location information is fed into trackers,which are based on Kalman filter or particle filter.To further improve the tracking accuracy,the interactive multiple model(IMM)technology is introduced into the tracker to consider the complex motion models of indoor targets.The experimental results show that the IMM-based tracker outperforms other trackers that use only single motion model when the target has more than one motion model.