Feature Aided Target Maneuver Detection Technology

Maneuverdetection belongs to the classic problem of abrupt change detection. It hasattracted considerable attention in activity identification, target tracking and recognition,and other communities, and has a wide range of military and civil applications, e.g., ma-neuvering target tracking and interception, automatic drive, car anti-crash, etc. However,at present, maneuver detection technology mostly depends on measurement residual anditsstatisticsinthefiltering. Itcannotobtainhighdetectionprobabilityandquickdetectionspeed simultaneously. Due to the close relationship between radar signatures and targetmotion mode, rich information of motion modes is embedded in the echoes. This pro-vides the feasibility of maneuver detection using radar signatures. Combining with aerialdefence, antimissile defence, long range air-to-air defence, and other application back-grounds, this thesis employs pulse Doppler radar system as the main object, and aimsto design a maneuver detector with low detection delay and high detection probabilityby studying radar signatures, which is entitled"feature aided target maneuver detectiontechnology".Chapter 1 analyzes the existing problems in the maneuver detection, and presentsthe route of our research work. We first analyze the major significance of feature aidedmaneuver detection combing with application backgrounds. The researching progress ofmaneuver detection at home and abroad is summarized and surveyed comprehensively.We then point out current shortcomings and further researching directions and work. Ourwork and organization of the thesis are introduced at last.Chapter 2 establishes the relationship between radar signatures and target motionmodes by analyzing the signatures of maneuvering targets. It provides theoretical guide-lines for feature aided maneuver detection. Firstly, the formulae of target pose angularrates under different motion modes are derived. The results show that normal acceleratedmaneuvering motion mode and nonmaneuvering motion mode are well classifiable. Theformer has attracted much attention by maneuvering target tracking and maneuver detec-tion. Secondly, the expressions of pose sensitive radar signatures including radar crosssections (RCS) and angular glint errors under different polarizations are obtained in thevector form based on a multiple-scatterer model. Mathematical description of high reso-lution Doppler profile (HRDP) is also given. Finally, taken a simple target consisting of two ideal scatterers as an example, we analyze the relationship between radar signaturesand pose angular rates, and come to the conclusions on the fluctuating characteristics ofthe radar signatures.Chapter 3 designs a novel maneuver detector based on the HRDP by applying theconclusions of signature analysis, and presents novel indices of performance evaluation.Firstly,existingalgorithmsareintroducedwhicharebasedonRCSandangularglinterror.We then modified the algorithms to accommodate to high data rate applications, and pro-posethenewdetectorsbasedonthecumulativeteststatistics. Secondly,thepropertiesandprofiling scheme of HRDP are introduced. A novel maneuver detection algorithm is pro-posed based on the HRDP. We extract the feature vector of HRDP resolution differences,regard maneuver detection as classification problem where nonmaneuvering and maneu-vering motion modes are two classes to be classified, and develop a maneuver detectorbased on the back propagation neural network. Thirdly, two indices, i.e., delay constantand convergence time, are proposed to evaluate the detection delay performance. Theyreflect the detection probabilities at the corresponding times with respect to the averagedetection delay, and reflect the dynamic performance of the detector more completely.Finally, the performance of feature aided maneuver detection algorithms is fully evaluat-ed by simulation experiments. The results show that the feature aided maneuver detectionalgorithms decrease maneuver detection delay effectively, improve detection probabilitygreatly, and enhance detection performance generally. In addition, the proposed maneu-ver detection algorithm which is based on HRDP outperforms the other two feature aidedalgorithms.Chapter 4 is a chapter of the experimental validation. The field and simulated exper-iments are carried out to validate the classifiability of motion modes based on radar signa-tures and the effectiveness of feature aided maneuver detectors. Firstly, aiming at radar IFsignal smapling problem in the field experiments, we discuss IF signal sampling systemdesign for coherent pulse radar. The generality design of synchronous trigger system ispresented, which includes constraints on sampling rate, nonuniformly sampled signal re-construction when inter-pulse coherent conditions are not met. A sampling and recordingsystem with high speed and huge capacity is developed based on the virtual instrumen-t technique, and is applied to the signal sampling tasks in the current field experimentsuccessfully. Secondly, the components of the experimental system are introduced, and the experimental scenarios are presented. Maneuver onset and termination detection isnot validated directly due to the limitations of many factors. Fortunately, experimentalresults show that the test statistics based on RCS and HRDP can distinct nonmaneuveringand maneuvering motion modes. It is a valuable exploration for feature aided target ma-neuver detection algorithms in engineering application. Finally, the effectiveness of thefeature aided maneuver detector in the application of maneuvering target tracking is vali-dated. The filtering error dynamics in terms of detection delay are presented and a upperbound for detection delay with given filtering errors is given, which provide theoreticalguidelinesformaneuverdetectordesign. Afeatureaidedvariable-structuremultiplemod-el approach for maneuvering target tracking is proposed. Simulation results show that theproposed algorithm outperforms the autonomous multiple model and interacting multiplemodel algorithms, and provides less computational complexity.Chapter 5 summarizes the researching work of the thesis, and presents the resultsand conclusions we obtained. The shortcomings and future work are also included.