Based On The Knowledge Of Auxiliary Particle Filter Tracking Algorithm Research Before Examination

It is an urgent need to improve the detection performance in view of the fact that the radar echoes are much weaker because of stealth technology and over-low altitude penetration. The track-before-detect (TBD) algorithms, accumulating the target energy over time to increase the signal-to-noise ratio (SNR), are therefore highly introduced for the enhancement of the detection performance. Particle filter (PF) based TBD (PF-TBD) algorithm, utilizing the approximable method to estimate the target states under the nonlinear/non-gaossian, is studied widely all over the world. Because the detecting capacity is limited by the growing complexity of the environment and the target variety, the knowledge-based system is used to improve the detection performance.The dissertation focuses on the studies of the PF-TBD algorithm combined with the available priori information. It mainly comprises:1. For solving the problem that it is difficult to effectively detect the high maneuvering target though the ESIR-TBD algorithm, a multiple models based ESIR-TBD algorithm using more models to describe the target movement is proposed. This algorithm can choose the matched model so as to detect the target effectively. Furthermore, the method of estimating the amplitude is provided.2. For solving the problem of lower precision of the particles initialization with the state space being larger for PF-TBD algorithm, a method for particles initialization based on competitive mechanism is proposed by utilizing the moving characteristics for the target. This algorithm can choose the optimal state sub-space and improves precision of the particles initialization though dividing the state sub-space.3. For solving the problem that amplitude statistical model is not matched for PF-TBD algorithm, an amplitude information based PF-TBD algorithm is proposed by incorporating the amplitude information. This algorithm increases the relativity of the neighboring frames for the amplitude by structure new likelihood function with the amplitude information so as to have better detection performance.4. For solving the problem of detecting the ground target under the condition of the complex road network, a novel PF-TBD algorithm is presented by utilizing the road information. This algorithm can improve the detection performance though reducing the dynamic model uncertainty and restraining target velocity.The effectiveness of the methods is validated by simulations. The knowledge-based PF-TBD algorithm outperforms the standard PF-TBD and can detect the weak target effectively.