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Research on Track-before-Detect Algorithm Based on Particle Filters

Author: ChenZuo
Tutor: JiHongBing
School: Xi'an University of Electronic Science and Technology
Course: Signal and Information Processing
Keywords: Particle filter Dim target Pre-test track Marginalized Quasi - Monte Carlo
CLC: TP391.41
Type: Master's thesis
Year: 2009
Downloads: 369
Quote: 3
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Abstract


Weak target detection and tracking infrared early warning system, a key technology for precision guidance system, satellite remote sensing systems. Under the influence of the long-distance attenuation and strong noise, the sensor receives the target very low signal-to-noise ratio, the traditional target detection and tracking method has been very difficult to meet the requirements. Recent years Pre-test track (TBD) an effective way to solve this problem, this method in one set of detection and tracking, and take full advantage of sensor data without threshold processing based on accumulated through time goal energy, thereby improving the signal-to-noise ratio, dim target detection and tracking. Based particle filter (PF) the TBD algorithm superior performance, but the particle filter generally requires a lot of random samples to ensure their performance, while a large number of random samples of the forecast, updated and re-sampling to calculate the particle filter is difficult to meet the requirements of real-time on the project . This paper focuses on using a variety of techniques to reduce the computational burden and improve the real-time nature of the algorithm based on particle filter TBD algorithm. First, through the analysis of infrared dim target model proposed a marginalized particle filter based TBD algorithm. The algorithm is characterized by marginalization method, the target speed linear Gaussian target state features state separation, and its use linear optimal Kalman filter, target position, intensity and nonlinear state is still with the particle filter processing. This not only reduces the dimension of the particle filter estimated state, greatly reducing the amount of computation, but also improve the detection performance and tracking accuracy of the algorithm in the low signal-to-noise ratio. Second, the faster convergence with an error intended to replace the traditional Monte Carlo (MC) particle filter integration method Monte Carlo (QMC) integration, and propose an improved algorithm: The proposed Monte Carlo-based Gaussian particle filter (QMC- GPF). Structured distributed sample points of the QMC integral with fewer the MC integrator of precision, the algorithm can guarantee the accuracy of the premise saves a lot of computational burden. Finally, in the QMC-GPF algorithm based on the convergence properties of the filter state covariance matrix tracking process to build judgment logic to achieve target detection. The algorithm is simple, small amount of calculation, simulation and experimental display of measured data, the algorithm has a good track of more than 3dB target detection capability.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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