Performance Comparison of TDOA CHAN Algorithm, TAYLOR Algorithm, and a Hybrid Algorithm through Bit Error Rate Simulation in Wireless Localization

In wireless localization technology, Time Difference of Arrival (TDOA) method is a crucial approach that utilizes signal arrival time differences at various base stations to estimate target positions. Among localization algorithms, CHAN and TAYLOR algorithms represent two classical positioning calculation methods, each with distinct advantages and limitations.

The CHAN algorithm, implemented through quadratic equation solving, demonstrates higher computational efficiency but suffers from degraded positioning accuracy under high-noise conditions. Code implementation typically involves matrix operations and least-squares solutions for the hyperbolic positioning equations. In contrast, the TAYLOR algorithm achieves higher precision through iterative optimization that approximates the true position, though it requires greater computational complexity and shows sensitivity to initial values. The TAYLOR implementation commonly uses gradient descent or Newton-Raphson methods for iterative refinement.

To leverage the advantages of both algorithms, we simulated a hybrid approach combining CHAN and TAYLOR algorithms. The implementation strategy first applies the CHAN algorithm to obtain an initial position estimate, which then serves as the starting point for TAYLOR algorithm's iterative refinement process. This sequential processing helps improve positioning accuracy while maintaining reasonable computational efficiency. Through bit error rate simulation experiments, we compared the performance of all three algorithms under varying noise levels.

Simulation results indicate: the CHAN algorithm performs well under low-noise conditions but exhibits significant error increases as noise levels rise; the TAYLOR algorithm maintains higher accuracy even in high-noise environments but requires substantial computational resources; while the hybrid algorithm achieves an optimal balance between computational efficiency and positioning accuracy, particularly demonstrating superior performance in medium to high-noise scenarios compared to individual algorithms.