A Learning Guide on Target Localization and Tracking

This learning guide provides an accessible yet thorough introduction to key technologies in target localization and tracking, applicable to scenarios such as radar, sonar, and wireless sensor networks. The book systematically explains several core algorithms:

Least Squares and Maximum Likelihood: Starting from fundamental parameter estimation theory, this section helps readers develop mathematical modeling thinking, suitable for target state prediction in linear environments. The implementation typically involves matrix operations and optimization techniques using built-in MATLAB functions like lsqnonlin or custom gradient descent algorithms.

Kalman Filter: Detailed derivation of its recursive formulas, with focused analysis of its optimal estimation capability under dynamic system noise. The guide discusses Extended Kalman Filter (EKF) approaches for handling nonlinear problems, including Jacobian matrix calculations and linearization techniques. Code examples demonstrate state prediction and measurement update cycles with customizable process and observation noise parameters.

Monte Carlo and Particle Filter: Addressing complex non-Gaussian distribution scenarios, this section explains approximate solution methods based on random sampling, comparing advantages and applicable boundaries with traditional filtering algorithms. Implementation details cover particle initialization, importance weighting, and resampling strategies to prevent degeneracy issues.

The guide's standout feature is its integration of theory and practice, demonstrated through complete simulation examples (such as motion trajectory prediction and multi-sensor data fusion) that reveal algorithm implementation details. All code includes comprehensive comments to help readers understand the correspondence between programming logic and mathematical formulas line by line. For developers seeking algorithm optimization, the book provides modular code structures that facilitate direct parameter adjustments or core function replacements (such as noise models and resampling strategies), enabling quick validation of improvement effects.

Whether for beginners learning fundamental theories or engineers solving practical localization problems, this manual offers complete references from mathematical derivation to engineering implementation.