Parameter Estimation Using SVD-TLS Algorithm

Implementation of SVD-TLS Algorithm for Parameter Estimation: This paper employs the SVD-TLS algorithm to estimate the power spectrum of observed signals through three key computational steps: (1) Compute SVD of the sample correlation function matrix R and store singular values with matrix V, (2) Determine the effective rank of R using normalized singular value method, (3) Estimate parameters via Total Least Squares approach.

Kalman Filter Simulation with Multi-Sensor Data Integration

This program provides a comprehensive Kalman filter simulation for target trajectory estimation, specifically enhanced with multi-sensor observations including range, velocity, and acceleration measurements. The implementation features a complete state-space model with proper noise covariance matrices, making it an ideal educational resource for colleagues familiar with Kalman filtering principles.

Kalman Filter Simulation for Target Tracking

This program provides a comprehensive Kalman filter simulation designed for estimating target motion trajectories. The implementation processes observation signals containing both distance and velocity measurements, utilizing state-space modeling and recursive prediction-correction cycles. The complete and validated codebase serves as an excellent educational resource for developers familiar with Kalman filtering principles, featuring modular implementation with clear separation between process models and measurement updates.