Adaptive Tracking of High-Maneuvering Targets Based on Multi-Feature Fusion Trajectory Clustering: LPI's Purpose

Area of Science:

• Electronic Warfare and Radar Systems
• Signal Processing and Target Tracking
• Multi-Sensor Data Fusion

Background:

• Passive sensors offer Low Probability of Intercept (LPI) benefits for target tracking.
• High-maneuvering targets with unknown motion models challenge traditional passive tracking algorithms.
• Existing algorithms struggle with tracking accuracy due to motion model uncertainties.

Purpose of the Study:

• To develop an advanced passive target tracking algorithm for high-maneuvering targets.
• To improve tracking accuracy and maintain LPI performance in complex scenarios.
• To propose a multi-sensor collaborative management model integrating passive and active sensors.

Main Methods:

• State transition matrix update-based extended Kalman filter (STMU-EKF) for passive tracking.
• Multi-feature fusion-based trajectory clustering for target state estimation.
• Multi-sensor collaborative management model with optimal sensor allocation based on error analysis.

Main Results:

• The STMU-EKF algorithm effectively estimates target states and updates the motion model.
• The multi-sensor model optimizes active radar and passive sensor allocation for improved accuracy.
• Simulation results demonstrate significant enhancement in tracking accuracy for high-maneuvering targets.

Conclusions:

• The proposed multi-sensor collaborative algorithm effectively addresses the limitations of passive-only tracking for maneuvering targets.
• The integration of active radar with passive sensors significantly boosts tracking precision.
• The developed approach successfully balances high tracking accuracy with the LPI requirements of combat platforms.