MATLAB Implementation of RSSI Localization Algorithm

RSSI localization algorithm is a wireless positioning technique based on Received Signal Strength Indicator, commonly used in indoor positioning and IoT device tracking scenarios. When implementing this algorithm in MATLAB, it's typically combined with Maximum Likelihood Estimation or Least Squares Method to optimize position calculation accuracy.

Core Implementation Approach Signal Propagation Model: RSSI values are generally modeled using logarithmic distance path loss models, requiring establishment of signal attenuation-distance relationships through actual measurements or theoretical formulas. Anchor Node Data: Assumes known coordinates of multiple anchor nodes (e.g., Wi-Fi access points) and their corresponding RSSI measurements as algorithm inputs. Maximum Likelihood Estimation: Estimates target position by maximizing likelihood function, requiring assumption of Gaussian-distributed noise and iterative solving for optimal solution using optimization functions. Least Squares Method: Linearizes nonlinear distance equations and directly solves target coordinates by minimizing error sum of squares, offering lower computational complexity but slightly reduced accuracy.

MATLAB Implementation Key Points Use `fit` function to calibrate signal attenuation parameters (e.g., path loss exponent). For Maximum Likelihood Estimation, utilize `fminsearch` or `lsqnonlin` optimization functions to minimize negative log-likelihood. Least Squares Method can be transformed into linear equations and efficiently solved using matrix operations (like backslash operator for linear system solving).

Extension Considerations Multipath effects and noise impact RSSI stability - consider adding filtering techniques (like Kalman filter) for data preprocessing. Hybrid approaches combining TOA (Time of Arrival) with RSSI can further enhance positioning accuracy in complex environments through sensor fusion techniques.