Filtering and Overlapping Data for Accuracy Enhancement of Doppler-Based Location Method.

Area of Science:

• Electrical Engineering
• Signal Processing
• Geospatial Intelligence

Background:

• Radio emitter localization is crucial for modern reconnaissance and mobile networks.
• The signal Doppler frequency (SDF) method uses Doppler frequency shifts (DFSs) for transmitter positioning.
• Existing SDF methods require enhancements for improved accuracy and efficiency.

Purpose of the Study:

• To improve the accuracy and efficiency of the signal Doppler frequency (SDF) method for radio emitter localization.
• To introduce advanced signal processing techniques, including filtering and overlapping, to the SDF method.
• To evaluate the performance of the enhanced SDF method in dynamic scenarios.

Main Methods:

• Implementing dedicated low-pass filtering at the DFS vector level.
• Introducing a novel two-level overlapping approach at signal sample and DFS vector levels.
• Conducting simulation studies with various filter types, cutoff frequencies, and overlapping parameters.
• Evaluating performance in a dynamic scenario with stationary transmitters and a moving receiver.

Main Results:

• Low-pass filtering at the DFS vector level reduced localization errors by approximately 30%.
• Signal sample level overlapping (10%) decreased localization errors more than twofold.
• DFS vector level overlapping increased the position update rate for localized objects.
• The enhanced SDF method demonstrated high effectiveness compared to direct position determination techniques.

Conclusions:

• The proposed enhancements significantly improve radio emitter localization accuracy using the SDF method.
• Advanced filtering and overlapping are critical for minimizing localization errors in dynamic environments.
• The optimized SDF method is highly suitable for real-world applications, including UAV-based localization sensors.