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Improved rao-blackwellized particle filter by particle swarm optimization
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نویسنده
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zhao z.-s. ,feng x. ,lin y.-y. ,wei f. ,wang s.-k. ,xiao t.-l. ,cao m.-y. ,hou z.-g. ,tan m.
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منبع
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journal of applied mathematics - 2013 - دوره : 2013 - شماره : 0
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چکیده
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The rao-blackwellized particle filter (rbpf) algorithm usually has better performance than the traditional particle filter (pf) by utilizing conditional dependency relationships between parts of the state variables to estimate. by doing so,rbpf could not only improve the estimation precision but also reduce the overall computational complexity. however,the computational burden is still too high for many real-time applications. to improve the efficiency of rbpf,the particle swarm optimization (pso) is applied to drive all the particles to the regions where their likelihoods are high in the nonlinear area. so only a small number of particles are needed to participate in the required computation. the experimental results demonstrate that this novel algorithm is more efficient than the standard rbpf. © 2013 zeng-shun zhao et al.
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آدرس
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shandong province key laboratory of robotics and intelligent technology,college of information and electrical engineering,shandong university of science and technology,qingdao 266590,china,school of control science and engineering,shandong university, China, shandong province key laboratory of robotics and intelligent technology,college of information and electrical engineering,shandong university of science and technology, China, shandong province key laboratory of robotics and intelligent technology,college of information and electrical engineering,shandong university of science and technology, China, shandong province key laboratory of robotics and intelligent technology,college of information and electrical engineering,shandong university of science and technology, China, shandong province key laboratory of robotics and intelligent technology,college of information and electrical engineering,shandong university of science and technology, China, shandong province key laboratory of robotics and intelligent technology,college of information and electrical engineering,shandong university of science and technology, China, shandong province key laboratory of robotics and intelligent technology,college of information and electrical engineering,shandong university of science and technology, China, state key laboratory of management and control for complex systems,institute of automation,chinese academy of sciences, China, state key laboratory of management and control for complex systems,institute of automation,chinese academy of sciences, China
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Authors
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