An Improved Compressive Sensing and Received Signal Strength-Based Target Localization Algorithm with Unknown Target...

Area of Science:

• Signal Processing
• Localization Algorithms
• Wireless Sensing

Background:

• Accurate target localization is crucial in various applications.
• Existing methods face challenges with unknown target populations and grid dimension effects.
• Received Signal Strength (RSS) based localization is widely used but can be imprecise.

Purpose of the Study:

• To propose a novel two-phase localization approach combining compressive sensing (CS) and RSS.
• To enhance position accuracy by addressing unknown target populations and grid dimension impacts.
• To improve the reliability of target localization in complex environments.

Main Methods:

• Formulating target localization as a sparse signal recovery problem in the coarse phase.
• Iteratively refining target positions within candidate grids using minimum residual error and least-squares in the fine phase.
• Employing threshold-based detection for re-estimating the recovery vector and determining target grids and population.

Main Results:

• The proposed two-phase CS and RSS approach significantly improves position estimation accuracy.
• The method effectively handles unknown target populations.
• Simulation results show superior performance compared to existing algorithms.

Conclusions:

• The developed two-phase localization strategy offers a robust solution for accurate target identification.
• This method provides a significant advancement in localization accuracy and target population estimation.
• The approach demonstrates high effectiveness and accuracy in simulation studies.