Research On Space-based Optical Target Detection And Tracking Real-time Processing Technology

The space-based optical surveillance system is used to timely detect,track,and identify enemy ballistic missiles,aircrafts,and spacecrafts that pose a threat to national security by deploying sensors on the space-based platform.It is of great significance to to enhance national strategic deterrence and defense capabilities to safeguard national security.Target detection and target tracking are the core technologies for information processing in space-based optical surveillance systems.It is the premise for achieving trajectory estimation and target identification,and has an important impact on the detection capabilities of space-based optical surveillance systems.Therefore,it has always been the focus and difficulty in remote monitoring applications.Space-based optical surveillance satellites need to keep a continuously 24-7 surveillance.In addition,the use of focal plane array sensor imaging system has the characteristics of wide range detection,large data rates for satellites up-and-down transmission.Consequently,the information processing system achieves a large scale of calculation for target detection,target tracking,and high processing performance and reliability.The real-time requirement of target detection and tracking is a new challenge for the design of system hardware/software architecture,parallel algorithm and reliability.Therefore,high-performance and high-availability parallel computing for real-time target detection and tracking technology is also a key technology for information processing.The second chapter studies the problem of detection of dim small moving targets in focal plane array sensor imaging.Single-frame image background suppression uses a finite variational model to estimate the details of the cloud edge and other details in the background image to improve the background suppression effect.Based on this,an optimization model is constructed for the detection of moving objects in image sequences to fully utilize the sequential image data of historical frames.Aiming at the problem of low computational efficiency of traditional batch processing algorithms,a theoretical derivation based on single-frame image background suppression model and finite variational model is proposed,and an improved sequential processing algorithm is proposed.Based on the finite variation theory,the moving target detection algorithm for image sequences can make use of multi-frame image data to suppress the background and give the detection results of dim small moving targets.Experimental results show that the motion detection algorithm for image sequences proposed in this paper can achieve better detection performance for dim small moving target.The third chapter focuses on the multi-target tracking under image measurement based on background suppression residual images to give full play to the advantage of the relative stability of the diffusion model of focal plane array sensors.In addition,related research has been carried out on the problem of image overlap in the image measurement and imaging overlap phenomenon in the vicinity of flight,as well as the limitation that traditional multi-target filtering algorithms cannot reasonably model.Based on the multi-object particle filter and labeled stochastic finite set model that can directly approximate the multi-object state distribution function,a multi-object particle filter algorithm with labels is proposed.Relevant theoretical derivations and numerical implementation algortithm are elaborated in detail,and the higher-order moment iteration filtering of multi-target states under image measurement is realized.Directly using image measurement for multi-target tracking can make full use of image data,avoiding information loss caused by hard decisions such as candidate points extraction,therefore multi-target filters based on image measurement can achieve high tracking performance.The fourth chapter further extends the research work to the tracking of area targets,and introduces the multi-target state smoothing to give full play to the advantage of high frame rate imaging of focal plane array sensors.Based on the finite set measurements extracted by low threshold fusion detection,the multi-target smoothing algorithm under the clutter parameter unknown observation model is studied given the temporal and spatial distribution characteristics of clutter sources in the measurement set.By estimating the state of the target and the distribution of clutter sources online,it is possible to have a certain self-adaptive ability for the unknown clutter distribution.The proposed target feature extraction method and adaptive clutter state estimation multi-target smoothing algorithm are applicable to various morphological targets such as point,patch,and area.It overcomes the limitations of low robustness of multi-target tracking in image measurement and high modeling accuracy of the observation model.The information processing system can form an on-line backtracking capability,giving more accurate target state estimation merely after a short-time group delay,which then effectively improving the multi-target tracking performance for the high frame-rate imaging systems.The fifth chapter focuses on the high-performance parallel computing technology for real-time detection and tracking of dim small moving targets in high frame rate focal plane array imaging.Based on the data parallelism of large field-of-view optical image,combined with OpenMP parallel tools,parallel image processing algorithms based on shared memory model are developed on the cluster architecture.For the finite set measurement formed by the fusion detection of dim small moving targets,a parallel particle PHD smoother is developed based on the MPI parallel tools.Consequently,smaller-scale hardware devices are required to implement real-time tracking of dim targets in large field-of-view with low thresholds,which has the ability of adapting to unknown background clutter.For those target tracking algorithms such as PHD smoother,the tracking performance is comparable to the TBD algorithm under image measurement,which provides technical support for the actual system.Based on the practical requirements of information processing for the space-based optical focal plane array imaging system,this paper proposes a real-time processing algorithm for detection and tracking of dim small targets.The research results of the dissertation improve the detection and tracking theory of dim small moving targets,provide theoretical guidance for the system design of information processing under the space-based optical focal plane array imaging system,and provide technical support for high-performance parallel computing for its engineering implementation.