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access icon free Adaptive iterated particle filter

  • Author(s): J.-Y. Zuo 1 ; Y.-N. Jia 2 ; Y.-Z. Zhang 1 ; W. Lian 3
    • View affiliations
    • Affiliations: 1: School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, People's Republic of China;
      2: 106 Institute, Qing'an Group Corporation Limited, Xi'an 710077, People's Republic of China;
      3: Department of Computer Science, Changzhi University, Changzhi 046011, People's Republic of China
  • Source: Volume 49, Issue 12, 06 June 2013, p. 742 – 744
    DOI:  10.1049/el.2012.4506 , Print ISSN 0013-5194, Online ISSN 1350-911X
© The Institution of Engineering and Technology
Received 04/01/2013, Published

The adaptive iterated particle filter (AIPF) is presented, where the importance density function is updated iteratively by the particle filter itself when necessary. By using a simulated annealing algorithm with an adaptive annealing parameter, the current measurement can be quickly incorporated into the sampling process, resulting in greatly improved sampling efficiency. Simulation results demonstrate the improved performance of the AIPF over the sampling importance resampling filter, unscented Kalman particle filter and auxiliary particle filter.

Inspec keywords: nonlinear filters; Kalman filters; simulated annealing; particle filtering (numerical methods); importance sampling

Other keywords: AIPF; sampling importance resampling filter; unscented Kalman particle filter; adaptive annealing parameter; sampling process; importance density function; adaptive iterated particle filter; auxiliary particle filter; simulated annealing algorithm

Subjects: Signal processing theory; Optimisation techniques; Optimisation techniques; Filtering methods in signal processing

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