Single Target Tracking Based on Current Statistical Model for Maneuvering Targets

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Resource Overview

Implementation of single target tracking using a current statistical model for maneuvering targets with Kalman filtering approach, including state prediction and measurement update cycles

Detailed Documentation

In this research, we implement single target tracking based on the current statistical model for maneuvering targets using Kalman filtering. The tracking algorithm continuously optimizes based on the target's current state through iterative prediction and correction steps. The implementation involves state vector initialization, covariance matrix configuration, and recursive updates using time update (prediction) and measurement update (correction) equations. We further explore optimization techniques to enhance tracking accuracy and stability, including adaptive process noise tuning and innovation-based residual analysis. Additionally, we investigate methodological extensions to handle more complex scenarios such as multiple target tracking scenarios using data association techniques and improved motion modeling for highly maneuverable targets through augmented state vectors or interacting multiple models (IMM) approaches.

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