Passive Radar Tracking in Clutter Using Range and Range-Rate Measurements.

Area of Science:

• Radar Systems Engineering
• Signal Processing
• Estimation Theory

Background:

• Accurate 3D target tracking is crucial for passive bistatic radar systems.
• Traditional Extended Kalman Filter (EKF) methods exhibit bias with missing or poor bearing data.
• Nonlinearities in passive bistatic radar tracking necessitate advanced filtering techniques.

Purpose of the Study:

• To develop a robust 3D target tracking algorithm for passive bistatic radars.
• To mitigate bias introduced by traditional EKF methods in scenarios with incomplete bearing information.
• To enhance tracking performance in cluttered environments using range and range-rate measurements.

Main Methods:

• Implementation of the Unscented Kalman Filter (UKF) to address nonlinearities in 3D tracking.
• Integration of the Probabilistic Data Association (PDA) algorithm with the UKF to manage environmental clutter.
• Utilizing range and range-rate measurements for state estimation.

Main Results:

• The proposed UKF-PDA framework successfully reduces estimation bias.
• Significant improvements in 3D target tracking accuracy were demonstrated through simulations.
• The method shows enhanced capability in handling missing or low-quality bearing data.

Conclusions:

• The UKF-PDA approach offers a superior alternative to traditional EKF for passive bistatic radar tracking.
• This framework significantly advances the state-of-the-art in passive bistatic radar target tracking capabilities.
• The proposed method provides a robust solution for accurate 3D tracking in challenging radar environments.