A Bayesian Surprise Approach in Designing Cognitive Radar for Autonomous Driving

Area of Science:

• Radar Systems Engineering
• Autonomous Systems
• Signal Processing

Background:

• Cognitive radar systems aim for autonomous adaptation to optimize performance.
• Minimizing estimation error is crucial for accurate target state prediction (e.g., velocity, distance).
• Bayesian surprise offers a potential metric for guiding adaptive measurement strategies.

Purpose of the Study:

• To investigate Bayesian surprise as a driving methodology for cognitive radar.
• To determine if cognitive radar can autonomously modify its internal model (waveform parameters) to enhance measurements.
• To evaluate the effectiveness of Bayesian surprise in minimizing estimation error over time.

Main Methods:

• Utilized Kalman filtering for state estimation under linear Gaussian state-space models.
• Implemented a decision process where the radar selects waveforms maximizing expected Bayesian surprise.
• Tested the system in vehicle-following scenarios within highway and urban driving environments.

Main Results:

• The proposed cognitive radar demonstrated improved estimation performance compared to state-of-the-art methods.
• Bayesian surprise-guided waveform selection led to reduced mean square relative error.
• The method showed robustness in single-target tracking across diverse driving conditions.

Conclusions:

• Bayesian surprise is an effective methodology for adaptive waveform selection in cognitive radar.
• Autonomous cognitive radar systems can leverage Bayesian surprise for enhanced target state estimation.
• The proposed approach offers a robust and superior alternative for real-time tracking applications.