Novel Approach to Nonlinear/Non-Gaussian Bayesian State Estimation¶
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, 107-113.
Cited by¶
1 citation across 1 artifact.
Each citation links to the sentence it supports in the citing article.
Domain-specific¶
- Particle Filter
- Gordon, Salmond, and Smith’s bootstrap filter made this concrete by representing the state density with random samples and recursively updating and propagating them in nonlinear, non-Gaussian tracking.
This sourceIntroduces the bootstrap filter as a random-sample implementation of recursive Bayesian filtering and demonstrates nonlinear bearings-only tracking.
- Gordon, Salmond, and Smith’s bootstrap filter made this concrete by representing the state density with random samples and recursively updating and propagating them in nonlinear, non-Gaussian tracking.
Verification¶
This reference passed the adversarial substantiation pipeline: it was checked to exist and to support the claim it is attached to. See how references were verified.
Registry ID ref:8af5bee041f0 · see in the full table