Ground Target Tracking Using an Airborne Angle-Only Sensor with Terrain Uncertainty and Sensor Biases

Area of Science:

• Aerospace Engineering
• Signal Processing
• Robotics

Background:

• Airborne angle-only sensors track ground targets but require known terrain height for 3D observability.
• Existing methods often assume accurate terrain height from Digital Terrain Elevation Data (DTED), ignoring its inherent uncertainty.
• Sensor biases are another significant source of uncertainty in angle-only measurements.

Purpose of the Study:

• To develop algorithms for estimating sensor biases using targets of opportunity.
• To propose a filtering framework for tracking targets with simultaneous terrain and sensor bias uncertainties.
• To derive the Posterior Cramer-Rao Lower Bound (PCRLB) for performance evaluation.

Main Methods:

• Algorithms for selecting optimal targets of opportunity for bias estimation.
• Methods for estimating sensor biases using measurements from selected targets.
• A filtering framework integrating terrain and bias uncertainty handling for target tracking.
• PCRLB derivation for single-target filtering with angle-only sensors, terrain uncertainty, and measurement biases.

Main Results:

• Accurate estimation of sensor biases is achievable using targets of opportunity.
• Proposed algorithms significantly improve target tracking accuracy in the presence of terrain and bias uncertainties.
• Monte Carlo simulations validate the effectiveness of the developed algorithms.

Conclusions:

• Handling terrain height and sensor bias uncertainties is crucial for accurate angle-only target tracking.
• The proposed methods provide a robust solution for improving tracking performance under realistic sensor and environmental conditions.
• This work offers a significant advancement in the field of passive target tracking with angle-only sensors.