摘要
随着科技的发展和现代战争的需要，导弹因具有射程远机动能力强进攻隐蔽作战用途广制导精度高杀伤威力大等特点，在现代战争中已经成为主要的中远程攻防武器因而，反导作战能力也成为现代战争的基本能力要求之一，导弹监视系统是反导作战的重要组成和基础天基监视系统利用其位置高观测范围大的优势，在弹道导弹主动段就能发现目标，及早给出预警信息，实现快速反应，是导弹监视系统的重要组成部分由于在远距离处观测目标，尽管目标自身的直径可能有几十米甚至几百米，但获得的目标成像面积小，可检测到的信号相对较弱，目标被大量噪声所淹没，导致图像的信噪比很低，弱小目标检测工作变得非常困难对于弱小点目标的检测正是一个亟待解决的关键问题，本文针对天基红外监视系统中的单传感器像平面弱小点目标轨迹检测与跟踪问题进行研究，重点分析探讨了天基红外图像背景杂波抑制技术未知目标数的检测前跟踪技术和多波段融合的检测前跟踪等关键技术，主要工作如下：
第二章对天基红外图像的特性进行分析并研究相应的背景杂波抑制算法首先，针对天基红外图像的云层杂波特性和目标在像平面的分布特性进行分析，在不同观测条件下，天基红外图像云层背景能量变化大；由于点扩散影响，目标在像平面的形状分布动态变化针对天基红外图像的两种特性，提出了基于改进的Markov时空域联合的背景抑制算法，通过仿真实验对所提算法的性能进行分析，实验结果验证了所提算法在背景杂波抑制目标信号保持方面的优势并且对背景抑制残差进行统计分析，为后续章节的研究提供了支撑
第三章研究了基于概率假设密度（Probability Hypothesis Density，PHD）滤波的检测前跟踪（Track-Before-Detect，TBD）技术主要从检测前跟踪的具体实际出发，重点研究概率假设密度滤波在检测前跟踪的应用，利用整体量测的思路，对PHD-TBD算法的核心环节粒子权重更新进行严格推导，同时改进其粒子采样方式，提出改进的PHD滤波检测前跟踪算法，给出详细实现步骤，最终通过仿真实验进行验证同时考虑PHD-TBD算法对目标数估计的起伏现象，引入平滑概念，提出基于PHD平滑器的检测前跟踪算法，进一步提高目标数估计的稳定度
第四章研究了势概率假设密度（Cardinazed Probability Hypothesis Density,CPHD）滤波器在检测前跟踪的应用，从标准CPHD滤波的粒子权重更新出发，结合检测前跟踪的实际，首次合理的推导出CPHD-TBD算法的粒子权重更新表达式；同时分析CPHD滤波目标势分布的物理意义，实现目标势分布更新计算在检测前跟踪的应用最终把CPHD滤波和TBD进行有效结合，首次提出基于势概率假设密度滤波的检测前跟踪算法，并给出其详细实现步骤仿真实验证明文章首次提出的CPHD-TBD算法与现有PHD-TBD算法相比，能更详细的传递目标分布信息，从本质上改变了PHD-TBD对目标数估计的方式，能更准确稳定估计目标数，实现对目标的发现和状态准确估计，性能明显更优
第五章研究基于多波段融合的检测前跟踪算法，从乘积形式多传感器PHD滤波出发，结合TBD对原始图像直接处理与计算似然的实际情形，合理推导出多波段融合PHD-TBD算法的粒子权重更新表达式，并详细阐述基于粒子实现的多波段融合PHD-TBD算法实现步骤多次蒙特卡罗仿真实验表明，文章算法通过多波段融合更新，能克服多波段更新顺序的影响，同时发挥多波段融合的优势，能在更低的目标信噪比条件下，实现对目标数和状态的精确估计同时研究基于多波段融合的CPHD检测前跟踪算法，以单波段的CPHD-TBD算法为基础，重点研究推导多波段融合的粒子权重更新表达式，同时结合检测前跟踪的物理实际，实现多波段融合势分布在检测前跟踪的应用，最后以仿真实验进行验证
With the development of science and the needs of modern warfare, the missile withthe feature of long range, mobility, wide combat application, high guidance accuracyand lethality, has become a major offensive and defensive weapons in modern warfare.Thus, the anti-missile combat capability has also become one of the basic capabilities ofmodern war requires, missile warning system is an important component of the missiledefense battle and foundation. To take advantage of its high location and largeobservation range, space-based early warning system will be able to detect target whilethe ballistic missile in the active course. So, space-based early warning system cansupply early warning information as soon as possible, which makes a rapid response.Due to the observation target at a long distance, even though the diameter of the targetmay be tens of meters or even hundreds of meters, the small size of the imaging of atarget obtained, can be detected in the signal is relatively weak, the target iscontaminated by noise, resulting in the signal noise ratio of the image is very low,which leads to dim target detection becomes very difficult. Dim point targets detectionis a key problem to be solved urgently. Dim point target trajectory detection andtracking of single sensor plane in space-based infrared early warning system isresearched in this paper. Early warning image background clutter suppressiontechnology, the unknown target detection tracking technology and multi-band fusionkey technologies are researched thoroughly, the main work is as follows:
In the second chapter, the characteristics of the image of the infrared early warningwere analyzed, and the background clutter suppression algorithm was also researched.First, the characteristics of infrared warning image clouds clutter and objectives in theimage plane were analyzed. The infrared warning image background clouds clutterdynamic range changes dramatically; the target in the image plane was a point target,due to the impact of the point spread, the target dynamic changes in the shape of theimage plane. An improved Markov time-space joint background suppression algorithmwas proposed, due to the feature of the image of infrared early warning system.Simulation experiments were conducted to analyze the performance of the proposedalgorithm, and the experimental results verify that the proposed algorithm performsexcellently in the background clutter suppression and target signal maintain. Thebackground suppression residuals were statistically analyzed, which provide support forthe subsequent chapters.
The third chapter mainly researched on the probability hypothesis density(Probability Hypothesis Density, PHD) filtering based track (Track-Before-Detect, TBD)technology. Mainly from the reality of TBD, the application of PHD filter in TBD wasstudied. The particle weight re-update of PHD-TBD algorithm was derived by employing the idea of the overall measurement. Meanwhile, the particle sampling mode,and an improved PHD filter for TBD algorithm was proposed. The implementationsteps of the proposed algorithm were described in detail. The proposed algorithm wasverified by simulation. Taking into account the fluctuation of PHD-TBD algorithm fortarget estimate, by employing smoothing concept, a PHD smoother based TBDalgorithm was proposed, which further improve the stability of target estimate.
The fourth chapter mainly researched on the application of cardinazed probabilityhypothesis density (CPHD) filter in TBD. Based on the particle weight re-update ofstandard CPHD filtering and combining the reality of TBD, the particle weightre-update expression of CPHD-TBD algorithm was reasonably deduced firstly.Meanwhile, the physical implication of the CPHD filter target cardinazed distributionwas analyzed. The target cardinazed distribution update calculation was applied in TBD.Finally, combining CPHD filtering and TBD effectively, an CPHD filtering based TBDalgorithm was proposed. The detailed implementation steps of the proposed algorithmwere given. The simulation experiments show that the CPHD-TBD algorithmoutperforms existing PHD-TBD algorithms. The CPHD-TBD algorithm transfer moredetailed target distribution information, which essentially changes the target numberestimate mode of PHD-TBD. The CPHD-TBD algorithm not only estimates the numberof targets more accurately, but also detects target accurately, which mean betterperformance.
The fifth chapter studied on multi-band fusion based TBD algorithm. Based on theproduct form multi-sensor PHD filtering and combining TBD directly deal with thecalculation of the likelihood of the case of the original image, the particle weightsre-update expressions of multi-band fusion PHD-TBD algorithm was first ly reasonablydeduced. The implementation steps of particle implementation based multi-band fusionPHD-TBD algorithm were elaborated. Several Monte Carlo simulation results show thatthe proposed algorithm overcome the impact of multi-band update order by usingmulti-band fusion update. Meanwhile, taking advantages of multi-band fusion, targetnumber and state precise estimation can be achieved in the lower target SNR condition.Multi-band fusion based CPHD-TBD algorithm was studied, either. On the basis ofsingle band CPHD-TBD algorithm, particle weight re-update expression of multi-bandfusion was researched especially. And combining with physical fact of TBD, multi-bandfusion cardinazed distribution was applied in TBD. Finally, the proposed algorithm wasverified by simulation experiments.
引文
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