Time Difference of Arrival Passive Localization Sensor Selection Method Based on Tabu Search.

Area of Science:

• Signal Processing
• Sensor Networks
• Optimization Algorithms

Background:

• Passive positioning systems rely on sensor networks for accurate localization.
• Balancing positioning accuracy with system resource consumption is a critical challenge in sensor network design.
• Existing methods may not efficiently address the trade-offs between accuracy and resource utilization.

Purpose of the Study:

• To propose a novel sensor selection method for Time Difference of Arrival (TDOA) passive positioning.
• To optimize sensor network configuration for enhanced positioning accuracy and reduced system consumption.
• To develop an efficient algorithm that approximates exhaustive search performance.

Main Methods:

• Developed a passive TDOA positioning model incorporating sensor position errors.
• Derived a constrained total least-squares (CTLS) solution and positioning error covariance matrix.
• Formulated sensor selection as an optimization problem minimizing positioning error covariance trace.
• Applied tabu search algorithm to efficiently solve the sensor selection problem.

Main Results:

• The proposed tabu search method achieves sensor selection performance close to exhaustive search.
• Demonstrated significant reductions in algorithm running time and improvements in timeliness.
• Successfully balanced positioning accuracy with system consumption in passive TDOA networks.

Conclusions:

• The tabu search-based sensor selection method is effective for passive TDOA positioning.
• This approach offers a computationally efficient alternative to exhaustive search for sensor optimization.
• The method provides a practical solution for improving the performance and efficiency of sensor networks.