Three-Dimensional Multi-Target Tracking Using Dual-Orthogonal Baseline Interferometric Radar.

Area of Science:

• Radar Signal Processing
• Estimation Theory
• Target Tracking

Background:

• Traditional multi-target tracking (MTT) methods require complex Doppler radar networks or expensive phased array antennas.
• Existing radar systems often suffer from high computational complexity and processing burdens.

Purpose of the Study:

• To develop an efficient algorithm for detecting and tracking multiple targets in 3D Cartesian space.
• To leverage range and 3D velocity measurements from dual-orthogonal baseline interferometric radar.
• To reduce the computational complexity associated with MTT.

Main Methods:

• Derivation of a nonlinear 3D velocity measurement function.
• Implementation of the global nearest neighbor (GNN) technique for data association.
• Utilizing an interacting multiple model estimator with a square-root cubature Kalman filter (IMM-SCKF) for state estimation.
• Employing a rule-based M/N logic for track management.

Main Results:

• The proposed algorithm demonstrates effective multi-target detection and tracking in various scenarios.
• Performance evaluation shows favorable track accuracy and reduced computational complexity compared to conventional methods.
• Monte Carlo simulations validate the algorithm's effectiveness and the IMM mean model probabilities.

Conclusions:

• The developed algorithm offers a computationally efficient solution for 3D multi-target tracking using interferometric radar.
• This approach provides a viable alternative to complex radar networks and costly phased array systems.
• The method achieves robust performance in terms of accuracy and efficiency for complex tracking scenarios.