Sensor Deployment Method Through Maximizing Reconfigurability to Reduce Degradation in TDOA Location System

Measurement anomalies may cause a sudden system performance degradation, especially in location systems based on time difference of arrivals (TDOAs) due to its limited number of redundant measurement instruments. Conventional approaches mitigate measurement anomalies through data processing techniques such as anomaly detection and system reconfiguration algorithms. However, these approaches may prove inadequate when anomalies affect critical system configurations. This study presents an innovative reconfigurability-enhanced deployment to reduce degradation in the presence of abnormal measurements. First, we introduce the model, algorithm, and performance evaluation of the TDOA location, along with a geometric dilution of precision (GDOP) metric to quantify the impact of system deployment independently of measurement uncertainty. Then, we elaborate on measurement anomalies and the existing strategies for system diagnosis and reconfiguration. Following this, we provide detailed analyses of system degradation risks under various strategies. Subsequently, we establish reconfigurability to assess the system’s tolerance to anomalies. An adapted evolutionary algorithm is proposed to design an optimal deployment that enhances reconfigurability. Finally, in accordance with real applications, we conduct simulations and semiphysical experiments to evaluate the performance of different deployments under anomalies. The results demonstrate that the proposed deployment effectively mitigates degradation risks while maintaining system performance.