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Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

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Dynamic compressed HRRP generation for random stepped-frequency radar based on complex-valued fast sequential

Peng You¹, Zhen Liu², Hongqiang Wang³

¹School of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China. ypnudt@126.com.

Sensors (Basel, Switzerland)

|May 13, 2014

Summary

This summary is machine-generated.

This study introduces a dynamic compressed sensing method for radar high resolution range profiles (HRRPs). The approach efficiently determines the optimal number of pulses, enhancing anti-jamming capabilities for uncooperative targets.

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Area of Science:

Radar Signal Processing
Compressed Sensing
Electromagnetics

Background:

Compressed sensing (CS) enables high resolution range profiles (HRRPs) with fewer pulses in stepped-frequency radar.
Reduced pulses shorten the coherent processing interval (CPI) and improve anti-jamming.
Determining the optimal number of pulses for CS-based HRRP is challenging in practical scenarios.

Purpose of the Study:

Propose a dynamic compressed sensing strategy for adaptive HRRP generation.
Develop an efficient algorithm for sequential HRRP updates.
Establish stopping rules to optimize pulse usage for HRRP estimation.

Main Methods:

A dynamic compressed sensing strategy updates HRRP estimates with sequential pulses.
A complex-valued fast sequential homotopy (CV-FSH) algorithm, based on group sparse recovery, enables efficient recursive updates.
Novel stopping rules are derived from HRRP characteristics to determine optimal pulse count.

Main Results:

The dynamic strategy adaptively determines the optimal number of pulses per CPI.
The CV-FSH algorithm avoids re-solving optimization problems from scratch.
Simulated and real data validate the effectiveness of the dynamic approach for HRRP generation.

Conclusions:

The proposed dynamic compressed sensing strategy effectively generates HRRPs by adaptively selecting the number of pulses.
The CV-FSH algorithm provides an efficient method for sequential sparse recovery in HRRP.
This dynamic approach is particularly suitable for radar applications involving uncooperative targets.