Research On Weak Target Track-before-detect Technologies For Space-based Infrared Image

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.