Obstacle-Aware 3D Positioning: Chan and Taylor WiFi Localization Algorithms

In obstacle-based 3D localization systems, Chan and Taylor WiFi positioning algorithms represent two commonly used technical approaches, primarily employed for accurate position estimation in complex environments. These algorithms demonstrate different adaptability characteristics when wireless signal propagation is affected by obstacles. Below are their core concepts and distinctive features.

Chan Algorithm The Chan algorithm is a Time Difference of Arrival (TDOA)-based localization method suitable for multi-base station collaborative measurement scenarios. In three-dimensional space, the target position is calculated by measuring time differences of signals received from different base stations. This algorithm linearizes nonlinear equation systems and solves for target coordinates using least squares estimation. When obstacles are present in the environment, signals may experience reflection or diffraction, leading to measurement errors. The Chan algorithm can enhance robustness through error compensation or by introducing weighted matrices. In code implementation, this typically involves constructing covariance matrices and applying weighted least squares optimization to handle non-line-of-sight (NLOS) errors.

Taylor Algorithm The Taylor algorithm is an iterative positioning method that approximates solutions to nonlinear positioning equations using Taylor series expansion. Its core concept involves progressively correcting errors from an initial position estimate until convergence to the true coordinates. In obstacle-dense environments, the Taylor algorithm may require modifications to signal propagation models, such as incorporating path loss exponents or reflection models. This method's advantage lies in its higher tolerance for initial estimation errors, though it comes with relatively higher computational complexity. A typical implementation would involve setting convergence thresholds and implementing gradient descent iterations with adaptive step sizes.

Impact of Obstacle Modeling In practical applications, obstacles significantly affect WiFi signal propagation characteristics. To improve positioning accuracy, obstacle modeling is typically required, such as simulating signal reflection and penetration behaviors through ray tracing techniques, or learning environmental features using machine learning methods. When integrated with Chan or Taylor algorithms, obstacle models can help correct signal parameters (like time of arrival or signal strength), thereby reducing positioning errors. Code implementation often involves creating environment maps and developing signal propagation correction functions based on material properties and obstacle positions.

Both algorithms have distinct advantages and limitations: the Chan algorithm offers high computational efficiency but is sensitive to noise, while the Taylor algorithm provides stronger adaptability but depends heavily on initial estimates. In complex 3D scenarios, advantages of both methods can be combined, or additional sensor data (such as from inertial measurement units) can be incorporated for fusion-based positioning. A hybrid implementation might involve using Chan for initial coarse positioning followed by Taylor refinement, or developing Kalman filter frameworks that integrate both approaches with sensor fusion.