Constrained L1-Norm Minimization Method for Range-Based Source Localization under Mixed Sparse LOS/NLOS Environments

Area of Science:

• Wireless communication
• Signal processing
• Geomatics engineering

Background:

• Achieving high positioning accuracy under mixed line-of-sight (LOS) and non-line-of-sight (NLOS) conditions remains a significant challenge.
• Existing methods struggle with the complexities introduced by NLOS bias, impacting both accuracy and computational efficiency.

Purpose of the Study:

• To develop a novel method for accurate and fast positioning under mixed LOS/NLOS environments.
• To mitigate the detrimental effects of NLOS bias on positioning accuracy and computational speed.

Main Methods:

• A constrained L1 norm minimization technique is proposed to address the sparse localization problem.
• The Time of Arrival (TOA)-based positioning problem is transformed into a sparse optimization problem by treating NLOS bias as outliers.
• An iterative method is employed to accelerate calculations.

Main Results:

• The proposed method effectively reduces the impact of NLOS bias, leading to improved positioning accuracy.
• The algorithm demonstrates a significant improvement in computational time compared to existing methods.
• The method successfully neglects NLOS status and associated errors, simplifying the positioning process.

Conclusions:

• The constrained L1 norm minimization method offers a robust solution for high-accuracy positioning in mixed LOS/NLOS scenarios.
• The algorithm provides a favorable balance between computational efficiency and positioning precision.
• This approach represents a significant advancement in addressing persistent challenges in wireless positioning.