Improved Kalman Filtering Algorithm Based on Levenberg-Marquart Algorithm in Ultra-Wideband Indoor Positioning.

Area of Science:

• Engineering
• Computer Science
• Signal Processing

Background:

• Current indoor positioning algorithms suffer from insufficient accuracy.
• Ultra-wideband (UWB) technology offers potential for precise indoor localization.

Purpose of the Study:

• To enhance the accuracy and stability of indoor positioning systems.
• To propose an improved UWB positioning algorithm incorporating advanced filtering techniques.

Main Methods:

• Utilized an alternative double-sided two-way ranging (ADS-TWR) algorithm for distance measurements.
• Employed the Levenberg-Marquardt algorithm to refine the Kalman filter's covariance matrix.
• Applied an improved Kalman filtering approach for precise indoor position estimation.

Main Results:

• MATLAB simulations confirmed the algorithm's feasibility and effectiveness.
• Experimental validation in Line-of-Sight (LOS) environments yielded average errors of 6.9 mm (X-axis) and 5.4 mm (Y-axis), with an RMSE of 10.8 mm.
• In Non-Line-of-Sight (NLOS) environments, average errors were 20.8 mm (X-axis) and 18.0 mm (Y-axis), with an RMSE of 28.9 mm.

Conclusions:

• The proposed UWB positioning algorithm demonstrates high accuracy and stability.
• The improved Kalman filtering significantly enhances positioning performance and convergence speed.
• This method offers a robust solution for accurate indoor localization challenges.