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báo cáo hóa học: " Multi-prediction particle filter for efficient parallelized implementation"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Multi-prediction particle filter for efficient parallelized implementation | Chu et al. EURASIP Journal on Advances in Signal Processing 2011 2011 53 http asp.eurasipjournals.eom content 2011 1 53 o EURASIP Journal on Advances in Signal Processing a SpringerOpen Journal RESEARCH Open Access Multi-prediction particle filter for efficient parallelized implementation Chun-Yuan Chu Chih-Hao Chao Min-An Chao and An-Yeu Andy Wu Abstract Particle filter PF is an emerging signal processing methodology which can effectively deal with nonlinear and non-Gaussian signals by a sample-based approximation of the state probability density function. The particle generation of the PF is a data-independent procedure and can be implemented in parallel. However the resampling procedure in the PF is a sequential task in natural and difficult to be parallelized. Based on the Amdahl s law the sequential portion of a task limits the maximum speed-up of the parallelized implementation. Moreover large particle number is usually required to obtain an accurate estimation and the complexity of the resampling procedure is highly related to the number of particles. In this article we propose a multi-prediction MP framework with two selection approaches. The proposed MP framework can reduce the required particle number for target estimation accuracy and the sequential operation of the resampling can be reduced. Besides the overhead of the MP framework can be easily compensated by parallel implementation. The proposed MP-PF alleviates the global sequential operation by increasing the local parallel computation. In addition the MP-PF is very suitable for multi-core graphics processing unit GPU platform which is a popular parallel processing architecture. We give prototypical implementations of the MP-PFs on multi-core GPU platform. For the classic bearing-only tracking experiments the proposed MP-PF can be 25.1 and 15.3 times faster than the sequential importance resampling-PF with 10 000 and 20 000 particles respectively. Hence the proposed MP-PF can enhance the efficiency