Removing False Targets for Cyclic Prefixed OFDM Sensing with Extended Ranging.

Area of Science:

• Wireless communication
• Signal processing
• Radar sensing

Background:

• Vehicular integrated sensing and communications (ISAC) increasingly uses cyclic prefixed Orthogonal Frequency Division Multiplexing (CP-OFDM) waveforms.
• Recent advancements enable CP-OFDM sensing to exceed traditional communication limits.
• A persistent challenge is the issue of false targets in CP-OFDM based sensing systems.

Purpose of the Study:

• To investigate the root cause of false targets in CP-OFDM sensing.
• To develop and validate a method for eliminating these false targets.
• To improve the accuracy and reliability of ISAC systems.

Main Methods:

• Analysis of CP-OFDM waveforms to identify the source of false targets.
• Derivation of mathematical relationships between false and true target characteristics.
• Development of a novel algorithm for false target removal.

Main Results:

• False targets are definitively linked to the periodic nature of cyclic prefixes (CPs) in CP-OFDM waveforms.
• The study establishes correlations between false target locations, strengths, and true targets.
• The proposed solution demonstrates a significant reduction in false alarm rates.

Conclusions:

• The periodic CPs in CP-OFDM are the cause of false targets in ISAC.
• The developed method effectively mitigates false targets, enhancing sensing performance.
• The proposed solution reduces false alarm rates by over 50% compared to existing methods, validating its effectiveness.