Research On Target Detection And Tracking Technologies For Space-based Infrared Surveillance System

Space-based infrared surveillance system has both the advantages of space-basedobserver and infrared sensor in many aspects, such as far sensing distance, largecoverage area, high measurement precision, concealment ability and so on. Space-basedinfrared surveillance system has been becoming the dominant measurement to keepstrategic targets under surveillance. The dissertation focuses on the problem of detectingand tracking targets in the focal plane array for the sensors of the space-based infraredsurveillance system. The key technologies are discussed and researched especiallyincluding the correction of the images non-uniformity, the suppression for thebackground clutter, the data association and tracking for multiple targets andtrack-before-detect (TBD) for dim targets at low signal to noise ratio(SNR). The maincontributions of this dissertation are demonstrated as follows:In chapter2, the studies are conducted on the correction of the imagesnon-uniformity and the suppression of the background clutter. Firstly, an improvedmethod based on Nerve-Network (NN) using the single scene image is proposed forcorrecting the non-uniformity. As the extensive experiments show that the proposedmethod achieves better performance and converges faster than the traditional NNmethod. Secondly, studies on the suppression of the background clutter for thegeo-stationary satellite is done with the two spatial-temporal fused filtering methodsconsequently proposed respectively based on Markov automatic regression(AR) modeland restricted sequential M-estimation. The experiments validate the superiority of theproposed method in the suppression of the clutter, the maintenance of the target signaland the improvement of the SNR over the being united and non-united spatial-temporalfused methods. Lastly, the studies are implemented on the suppression of thebackground clutter for the moving satellite sensors with low orbit. Two stagedspatial-temporal fused methods are discussed respectively on the images registration byfeatures and on the parameters of the plane with experiments following to compare andanalysis their performance. The results show that the presented algorithms outperformthe spatial filtering ones. The aforementioned researches can serve as the foundation forthe succeeding chapters.In chapter3, the issue of the dada association and multi-target tracking is studied.Firstly, the measurement model and target dynamic model on focal plane are bothestablished respectively for the broom-scanning and cone-scanning sensors. Secondly,for the broom-scanning sensor studies are focused on the multi-target data associationand tracking based on Markov chain Monte Carlo with importance sampling(IS-MCMC) to judge the target existence and then track it. The algorithm is validatedby experiments. Compared to the traditional methods such as MHT, MCMC and SMC-PHD, IS-MCMC has the superiority in data association and target detection. astly,to track multi-target under nonlinear observation for the cone-scanning sensor, a methodfor data association and tracking is proposed on augmented unscented Kalman filter(AUKF) and optimized integrated probability multi-hypothesis tracking (OIPMHT).Further OIPMHT is combined with interactive multiple model (IMM-OIPMHT) totrack the target with nonlinear motion. The methods including IMM-OIPMHT,IS-MCMC, IPMHT and so on are analyzed in comparison by simulations of thespaced-based infrared surveillance system, which show that IMM-OIPMHT works asthe best one.In chapter4, the studies are focused on TBD for the dim targets with low SNR.Firstly, the target dynamic and measurement model are set up for TBD. Secondly,heuristic particle filter on multiple-model (MM-PF) for TBD is proposed on theimprovement of the original single target MM-PF TBD, which is extended to thespace-based infrared surveillance system with unknown target number, launched andburned out time. The advantage of heuristic MM-PF TBD is validated over MM-PFTBD, histogram probability multi-hypothesis tracking(H-PMHT TBD), dynamicprogramming algorithm(DPA TBD) and so on. Lastly, to settle the low efficiency of theiterative searching of heuristic MM-PF, probability hypothesis density (PHD) TBDproposed recently is introduced and improved in combination with multiple-model(MM-PHD TBD) to track the dim targets with nonlinear motion. The experiments forspace-based infrared surveillance system with dim targets show that MM-PHD TBD assame as heuristic MM-PF outperforms other TBD algorithms such as PHD, H-PMHTand DPA.