Tracking Multiple Targets from Multistatic Doppler Radar with Unknown Probability of Detection

Area of Science:

• Radar Systems Engineering
• Signal Processing
• Estimation Theory

Background:

• Multistatic radar systems face challenges like data association, noise, missed detections, and false alarms.
• Existing multitarget tracking methods often assume known detection probability, leading to potential estimation errors.

Purpose of the Study:

• To develop a robust method for multitarget tracking in multistatic Doppler radar systems with unknown detection probability.
• To address limitations of current approaches that rely on the assumption of known detection probability.

Main Methods:

• A closed-form labeled multitarget Bayes filter was employed for tracking.
• The method accommodates unknown and time-varying target states and detection probabilities.
• It accounts for clutter, misdetections, and association uncertainties inherent in radar measurements.

Main Results:

• The proposed algorithm effectively tracks multiple targets under complex and uncertain conditions.
• Numerical simulations demonstrated the efficiency and accuracy of the developed tracking method.
• The approach mitigates biases caused by the unknown detection probability assumption.

Conclusions:

• The developed method provides a more reliable solution for multitarget tracking in multistatic Doppler radar.
• This work advances radar signal processing by handling unknown detection probabilities, crucial for real-world applications.
• The proposed filter offers improved estimation performance in challenging radar environments.