Beyond the Kalman Filter: Particle Filters for Tracking Applications (Artech House Radar Library (Hardcover))
For most tracking applications the Kalman filter is reliable and efficient, but it is limited to a relatively restricted class of linear Gaussian problems. To solve problems beyond this restricted class, particle filters are proving to be dependable methods for stochastic dynamic estimation. Packed with 867 equations, this cutting-edge book introduces the latest advances in particle filter theory, discusses their relevance to defense surveillance systems, and examines defense-related applications of particle filters to nonlinear and non-Gaussian problems.
With this hands-on guide, you can develop more accurate and reliable nonlinear filter designs and more precisely predict the performance of these designs. You can also apply particle filters to tracking a ballistic object, detection and tracking of stealthy targets, tracking through the blind Doppler zone, bi-static radar tracking, passive ranging (bearings-only tracking) of maneuvering targets, range-only tracking, terrain-aided tracking of ground vehicles, and group and extended object tracking.
Why Read This Book
You should read this book if you need to move beyond Kalman-based estimators and implement robust state estimation for nonlinear, non‑Gaussian tracking problems. It gives you practical particle filter algorithms, design choices (importance densities, resampling, regularization), and radar/target-tracking examples so you can apply Sequential Monte Carlo methods in real systems.
Who Will Benefit
Engineers and researchers working on radar/target tracking or statistical signal processing who know Kalman filters and need tools for nonlinear/non‑Gaussian estimation and real-world tracking applications.
Level: Advanced — Prerequisites: Undergraduate linear algebra and probability theory, familiarity with state‑space models and the Kalman filter/Bayesian estimation; comfort with mathematical derivations and some programming for algorithm implementation.
Key Takeaways
- Implement core particle filter algorithms (bootstrap/SIR, auxiliary particle filters, and resample‑move variants) from pseudocode.
- Analyze and mitigate particle degeneracy through resampling, regularization, and choice of proposal distributions.
- Apply particle filters to radar tracking scenarios (bearing‑only, maneuvering targets, ballistic trajectories) and assess performance.
- Perform joint state and parameter estimation using sequential Monte Carlo techniques.
- Compare particle filters' strengths and limitations relative to Kalman and extended/unscented filters for non‑Gaussian/nonlinear problems.
Topics Covered
- 1. Introduction and Motivation: Beyond the Kalman Filter
- 2. Bayesian Filtering Fundamentals
- 3. Importance Sampling and Sequential Importance Resampling (SIR)
- 4. Resampling Algorithms and Particle Degeneracy
- 5. Choice of Importance Densities and Proposal Design
- 6. Advanced Particle Filters: Auxiliary, Regularized, and Rao‑Blackwellized PFs
- 7. Particle MCMC and Smoothing Methods
- 8. Parameter Estimation and Joint State/Parameter Filtering
- 9. Data Association and Multi‑target Tracking with SMC
- 10. Implementation Issues, Complexity, and Practical Considerations
- 11. Radar and Defense Applications: Bearings‑only, Ballistic, and Maneuvering Targets
- 12. Performance Evaluation, Metrics, and Case Studies
- 13. Conclusions and Further Research Directions
Languages, Platforms & Tools
How It Compares
More applied and tracking‑oriented than the canonical 'Sequential Monte Carlo Methods in Practice' (Doucet et al.), and more specialized for radar/tracking than general Bayesian filtering texts like Särkkä's 'Bayesian Filtering and Smoothing'.
