A Robust Wireless Sensor Network Localization Algorithm in Mixed LOS/NLOS Scenario.

Area of Science:

• Computer Science
• Electrical Engineering
• Signal Processing

Background:

• Received Signal Strength Indication (RSSI) localization is common but suffers from Non-Line-of-Sight (NLOS) errors indoors.
• Existing methods often require prior propagation models and yield low accuracy in mixed LOS/NLOS scenarios.

Purpose of the Study:

• To propose a novel localization algorithm, Gaussian mixed model based non-metric Multidimensional (GMDS), for accurate indoor target localization in mixed LOS/NLOS environments.
• To develop a method that does not require prior knowledge of the propagation model.

Main Methods:

• RSSI values are estimated using a Gaussian Mixture Model (GMM).
• A dissimilarity matrix is constructed to generate relative node coordinates via Multidimensional Scaling (MDS).
• Target coordinates are computed through coordinate transformation using anchor and relative coordinates.

Main Results:

• The GMDS algorithm effectively reduces NLOS errors.
• GMDS demonstrates higher accuracy in indoor mixed LOS/NLOS localization compared to existing methods.
• The algorithm remains effective even with multiple NLOS conditions.

Conclusions:

• GMDS offers a robust and accurate solution for indoor target localization in challenging mixed LOS/NLOS conditions.
• The proposed method eliminates the need for prior propagation model information, simplifying application.