Global systems for mobile position tracking using Kalman and Lainiotis filters.

Area of Science:

• Engineering
• Computer Science
• Signal Processing

Background:

• Accurate positioning is crucial for Global Systems for Mobile (GSM) applications.
• Existing tracking models may have limitations in computational efficiency or accuracy.

Purpose of the Study:

• To develop and compare time-invariant models for GSM position tracking.
• To evaluate the performance and computational burden of various filtering techniques.

Main Methods:

• Development of two time-invariant models for GSM position tracking (simultaneous or separate axis movement).
• Implementation and comparison of classical Kalman Filter, Lainiotis Filter, and a Join Kalman Lainiotis Filter.
• Proposal of a Finite Impulse Response (FIR) implementation for steady-state filters.

Main Results:

• All time-invariant and steady-state filters demonstrated consistent behavior across both proposed models.
• Significant differences in computational burden were observed among the evaluated filters.
• The FIR implementation offers an alternative that bypasses the need for prior estimations.

Conclusions:

• The choice of filter impacts computational efficiency in GSM position tracking.
• The proposed FIR implementation provides a viable alternative for specific scenarios in GSM tracking.