Research On Ground Obscured Target Detection For Imaging Laser Radar

Ground target detection is an important research topic in the field of air-to-groundreconnaissance, precision striking and automatic target recognition. Ground targets arelikely to be obscured by various artificial or natural objects, and it is difficult fortraditional2-D imaging systems to obtain effective target information. The beam ofimaging laser radar (ladar) is narrow and directional. As a result, it can penetrate theoccluder to detect a fraction of the target surface to a certain extent, and obtain the rangeimage and3-D point cloud data which reflect the geometric structure of the target scene.The imaging ladar facilitates information acquisition for obscured target detection, butthe imaging data is more complex in the obscure environment. Therefore, it becomes atechnical problem how to extract target due to the interference of noise, clutter and lackof data. Based on this application background, this dissertation adopts ‘data acquisition,data preprocessing, target detection’ as the overall line of thought, and studies datamodeling and simulation, noise suppression, data registration, and detection algorithm,with the goal to solve the problems of obscured target detection of imaging ladar, andprovides foundation for related applications.Introduction describes the background and significance of the topic considered.The study status of the development and application, target detection technology, andobscured target detection methods for imaging ladar are summarized and surveyed. Ourwork and organization of the dissertation are introduced at last.Chapter2studies the modeling and simulation methods for the full-waveformimaging ladar, which aims at solving the problem of data acquisition and provides datasupport for the related algorithms. Firstly, the basic principle of the full-waveformimaging ladar is described, and the formation process of full-waveform echo signal isrealized. Secondly, focusing on problems in modeling and simulation, we study andmodel the three major components of a simulation system, which are the method ofobtaining projection points of the laser beam in the target scene, the action processbetween the ladar signal and target scene, and the receiver noise and range-gated mode.There are many ray-intersection operations in the obtaining process of projection points,and traditional algorithms are inefficient. To solve this problem we propose a novelray-intersection fast algorithm based on the space boundary of the laser beam. Thirdly,we introduce the overall structure, specific steps and flow chart of the simulation system,and construct a simulation system of full-waveform imaging ladar. The system canprovide imaging data under a variety of conditions. Finally, combining theoreticalanalyses and simulation experiments, we analyze and verify the reliability of thesimulation system from three aspects, which are waveform characteristics of thefull-waveform echo signal, range-gated mode, and noise characteristics of the imaging data.Chapter3studies the noise suppression algorithm of ladar data to provide highquality data for subsequent processing. Firstly, based on analysis of the distributioncharacteristics of all kinds of noise in the ladar data, we point out that the effect of rangeanomaly is the most important factor. Then combining different behaviors of rangeanomaly in the range image and3-D point cloud data, the suppression algorithms arestudied separately. For the range image, we propose a range anomaly suppressionalgorithm based on neighborhood pixels detection, according to the fact that there are atleast a small amount of range-similar pixels in the neighborhood of the normal pixel.For the3-D point cloud data, there are significant differences in the number of points inspace neighborhood (NPSN) between the normal and anomalous point, and ananomalous point suppression algorithm based on NPSN detection is proposed. Wevalidate the range anomaly suppression performance and the capability of protectingtarget data of the two proposed algorithms by experiments, taking into account theirdenoising performance and implementation efficiency.Chapter4studies the ladar data registration algorithm, which is to solve theproblem of data insufficiency in the obscure environment and provides foundation forobscured target detection. Firstly, through analyzing and summarizing the backgroundrequirements and the study status of multi-view registration algorithm, we find that thetwo-view data registration is fundamental and the iterative closest point (ICP) algorithmlies in the core. Secondly, combining the data characteristic in the obscure environment,we analyze and improve the application strategies of the ICP’s main steps, and propose atwo-view data registration algorithm based on improved ICP. Thirdly, a multi-view dataregistration algorithm based on aggregating the adjacent frames which are alreadyregistered is proposed. Based on the sequence registration strategy, it increases theoverlap region between the pending registration frames by aggregating, and furtherimproves the registration accuracy. Experiment results validate that the two proposedalgorithms have greater robustness and higher registration accuracy under conditions ofdifferent overlap regions and initial errors. Finally, we analyze the impact factors of theimproved ICP through the means of a combination of theoretical and experimentalanalysis. The results show that the effect of view-interval and imaging mode of theregistration data is more significant, and the effect of the carrier position and attitudeerrors is less.Chapter5studies the ground obscured target detection algorithm for segmentingand extracting the data in the region of interest (ROI). Based on the analysis of spatialdistribution characteristics of various ground-objects in the obscure environment, wefocus on three spatial filtering methods to filter out the clutter data step by step. Firstly,we study a filtering method based on the estimation of ground digital elevation model(DEM) and segmentation by elevation threshold to filter out the clutter data on the ground and higher objects. To reliably estimate the DEM, a filter based on improvedprogressive multi-scale mathematic morphology is proposed by improvingmorphological opening operation. Secondly, we propose a classification-detectionalgorithm of the local spatial distribution characteristics of3-D point based on theprincipal component analysis (PCA). It extracts the principal component andcontribution rate of the data in voxel neighborhood, then classifies and detects thescattered points, linear points and planar points according to the relationship of pointdistribution state and contribution rate of principal component. Thirdly, we propose amethod of target region detection based on ground shadow analysis to filter out theclutter data of interference objects which cannot form ground shadow of a large area.According to the inherent physical characteristics of the targets of interest that they arenot penetrable to laser beam and can form ground shadow of a large area, the targetregion is detected by detecting the ground shadow of a large area. We derive therelationship of the algorithm’s main parameters and the geometrical dimensions of thetarget of interest. Based on the spatial filtering results, the sets of data points for eachsuspected target region are obtained by orderly using the hierarchical clustering methodbased on closest distance threshold and clustering analysis method based on positionalrelationship of minimum bounding rectangle. By combining with the priori geometryinformation of target, false alarms are further removed, and the3-D point cloud data ofROI is obtained. Finally, we sum up a general framework of the detection method forground obscured target, to provide a foundation for the subsequent target classificationand identification.Chapter6summarizes the research work and innovative achievements of thedissertation. The shortcomings and future work are also included.