Pitfalls and possibilities of radar compressive sensing

Area of Science:

• Remote Sensing
• Signal Processing
• Electrical Engineering

Background:

• Radar remote sensing generates large datasets, necessitating efficient data compression techniques.
• Compressive Sensing (CS) offers a novel approach to acquiring and processing signals below the Nyquist rate.
• Existing radio-frequency (RF) practices may already incorporate principles similar to CS.

Purpose of the Study:

• To investigate the applicability and system-level challenges of compressive sensing (CS) in radar remote sensing.
• To review practical issues that researchers must consider when applying CS to radar systems.
• To identify potential benefits of recent CS advancements for RF practitioners.

Main Methods:

• Survey of system-level issues in radar data compression using CS principles.
• Analysis of historical RF practices for elements related to CS.
• Review of recent CS literature for relevant findings.

Main Results:

• Identification of critical system-level challenges for CS in radar remote sensing.
• Examples of existing RF techniques that share characteristics with CS.
• Highlighting promising CS advancements applicable to radar.

Conclusions:

• System-level considerations are crucial for successful CS implementation in radar.
• Historical RF practices offer insights into CS-like methodologies.
• Recent CS developments present significant opportunities for radar remote sensing.