Estimation with Applications to Tracking and Navigation: Theory Algorithms and Software
Expert coverage of the design and implementation of state estimation algorithms for tracking and navigation
Estimation with Applications to Tracking and Navigation treats the estimation of various quantities from inherently inaccurate remote observations. It explains state estimator design using a balanced combination of linear systems, probability, and statistics.
The authors provide a review of the necessary background mathematical techniques and offer an overview of the basic concepts in estimation. They then provide detailed treatments of all the major issues in estimation with a focus on applying these techniques to real systems. Other features include:
- Problems that apply theoretical material to real-world applications
- In-depth coverage of the Interacting Multiple Model (IMM) estimator
- Companion DynaEst(TM) software for MATLAB(TM) implementation of Kalman filters and IMM estimators
- Design guidelines for tracking filters
Suitable for graduate engineering students and engineers working in remote sensors and tracking, Estimation with Applications to Tracking and Navigation provides expert coverage of this important area.
Why Read This Book
You will learn how to design, analyze, and implement practical state estimators that work in real-world tracking and navigation systems, combining solid theory with application-focused examples. This book uniquely balances linear systems, probability, and statistics to give you tools that scale from single-target Kalman filters to multi-target data-association algorithms used in radar, sonar, and navigation.
Who Will Benefit
Practicing engineers, graduate students, and system designers with some mathematical background who need to build or evaluate state estimators for tracking, navigation, or sensor-fusion systems.
Level: Advanced — Prerequisites: Linear algebra (matrix operations, eigenvalues), probability and stochastic processes (random variables, covariance), basic state-space systems and control concepts, and familiarity with signal processing fundamentals.
Key Takeaways
- Derive and implement optimal linear estimators such as the Kalman filter and its discrete/continuous variants.
- Extend linear filtering to nonlinear problems using the extended filter and related approximations for navigation tasks.
- Design and apply data-association techniques (e.g., JPDA, MHT) for robust multi-target tracking.
- Model sensor and process noise statistically and use performance bounds (e.g., CRLB) to evaluate estimator performance.
- Integrate heterogeneous sensors (radar, INS, GPS) in navigation fusion architectures and handle practical implementation issues.
- Analyze numerical stability, initialization, and tuning trade-offs for real-time estimator deployment.
Topics Covered
- 1. Introduction and Motivation: Estimation in Tracking and Navigation
- 2. Mathematical and Statistical Preliminaries
- 3. Linear State-Space Models and Observability
- 4. The Kalman Filter: Derivation and Properties
- 5. Continuous- and Discrete-Time Filtering and Smoothing
- 6. Nonlinear Filtering and the Extended Kalman Filter
- 7. Performance Analysis and Bounds (CRLB and Error Covariance)
- 8. Data Association and Multi-Target Tracking (JPDA, MHT)
- 9. Tracking Algorithms and Practical Considerations
- 10. Sensor Fusion: INS/GPS and Heterogeneous Measurements
- 11. Implementation Issues: Numerical Stability and Real-Time Constraints
- 12. Case Studies and Engineering Problems
- Appendices: Useful Results, Problem Solutions, and Reference Material
Languages, Platforms & Tools
How It Compares
Compared to Grewal & Andrews' 'Kalman Filtering: Theory and Practice', Bar‑Shalom offers broader, application-centric coverage of multi-target tracking and data association, while Simon's 'Optimal State Estimation' is more concise and tutorial-focused on modern estimator variants.
