Linearized RSSI-Based Coordinate Estimation

This paper explores a linearized RSSI-based coordinate estimation method for localization. The core approach involves constructing an RSSI matrix using three-dimensional RSSI measurements and computing error curves across K iterative cycles. The method employs matrix operations to linearize the nonlinear relationship between RSSI values and distance, where key computations include matrix inversion and least-squares optimization. Through iterative refinement, this technique achieves enhanced positioning accuracy. Particularly suitable for indoor target localization, the method demonstrates robustness against complex signal interference common in indoor environments. Its implementation typically involves signal preprocessing, matrix initialization, and iterative error minimization algorithms. We anticipate this approach will play a significant role in future localization technologies, delivering more precise and reliable positioning experiences through its systematic error reduction mechanism.