A Localization and Tracking Approach in NLOS Environment Based on Distance and Angle Probability Model

Area of Science:

• Engineering
• Computer Science
• Signal Processing

Background:

• Indoor localization in non-line-of-sight (NLOS) environments presents significant challenges due to signal obstructions.
• Existing methods often suffer from reduced accuracy and high computational costs in complex indoor settings.

Purpose of the Study:

• To develop an advanced optimization algorithm for precise indoor localization.
• To specifically address and mitigate errors in non-line-of-sight (NLOS) propagation scenarios.

Main Methods:

• A distance and angle probability model was developed to identify NLOS propagation using sampling information.
• Maximum Likelihood Estimation (MLE) was applied to minimize distance errors within NLOS conditions.
• A modified Monte Carlo method was employed to optimize target positioning with reduced computational complexity.
• Extended Kalman Filtering (EKF) was integrated for robust localization.

Main Results:

• The proposed algorithm effectively identifies and accounts for NLOS propagation.
• Significant reduction in localization errors was achieved through the MLE and modified Monte Carlo methods.
• The integration of EKF further enhanced the localization precision.

Conclusions:

• The developed optimization algorithm demonstrates superior performance in indoor NLOS localization.
• The combination of probability modeling, MLE, Monte Carlo, and EKF offers a robust solution for accurate positioning.