Bayesian methodsBayesian / computational
动态粒子滤波器
动态粒子滤波器是一种序贯蒙特卡洛算法，它通过维护一组加权的随机样本——粒子——来跟踪随时间演化的隐藏状态，每个粒子代表一个可能的轨迹。随着新观测值的到来，粒子权重通过似然函数进行更新，并通过重采样来保持粒子群，从而在完全非线性和非高斯的情况下将表示集中在最可能的状态区域。
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来源
- Doucet, A., de Freitas, N. & Gordon, N. (Eds.). (2001). Sequential Monte Carlo Methods in Practice. Springer. ISBN: 978-0387951461
- Gordon, N. J., Salmond, D. J. & Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F – Radar and Signal Processing, 140(2), 107–113. DOI: 10.1049/ip-f-2.1993.0015 ↗
如何引用本页
ScholarGate. (2026, June 3). Dynamic Particle Filter for Sequential State Estimation. ScholarGate. https://scholargate.app/zh/bayesian/dynamic-particle-filter
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