Mobile location with NLOS identification and mitigation based on modified Kalman filtering

Area of Science:

• Wireless communication
• Signal processing
• Geospatial positioning

Background:

• Accurate wireless location is crucial for mobile devices.
• Mixed line-of-sight (LOS) and non-line-of-sight (NLOS) environments pose significant challenges to location accuracy.
• Existing algorithms often struggle with NLOS errors, impacting reliability.

Purpose of the Study:

• To develop a robust mobile location algorithm for enhanced accuracy and reliability in mixed LOS/NLOS environments.
• To reduce the impact of NLOS errors on mobile node (MN) positioning.
• To provide a location method that does not require prior knowledge of NLOS error distributions.

Main Methods:

• Utilizing an extended Kalman filter (EKF) modified during the updating phase.
• Estimating NLOS bias directly using a constrained optimization method.
• Implementing a low-complexity identification method based on innovation vectors to distinguish between LOS and NLOS conditions.

Main Results:

• The proposed algorithm significantly reduces location errors compared to iterated NLOS EKF and conventional EKF algorithms.
• The method effectively handles mixed LOS/NLOS conditions without needing statistical NLOS error knowledge.
• Complexity experiments confirm the algorithm's suitability for real-time applications.

Conclusions:

• The developed algorithm offers a robust and accurate solution for mobile location in challenging wireless environments.
• The innovation lies in the modified EKF and NLOS bias estimation, improving performance.
• The algorithm is efficient and supports real-time tracking, making it practical for various applications.