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Fast Implementation of Approximated Maximum Likelihood Parameter Estimation for Frequency Agile Radar under Jamming

Yang Zhao¹, Jianxin Wu², Zhiyong Suo¹

¹National Laboratory of Radar Signal Processing, Xidian University, Xidian 710071, China.

Sensors (Basel, Switzerland)

|April 9, 2020

Summary

This study presents an efficient algorithm for frequency agile radar (FAR) to estimate target parameters despite jamming. The method suppresses jamming and analytically solves for parameters, significantly reducing computational load.

Keywords:

frequency agility jamming suppression maximum likelihood estimation

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Area of Science:

Radar Systems Engineering
Signal Processing
Electronic Warfare

Background:

Jamming poses a significant challenge to radar systems, degrading performance.
Accurate target parameter estimation is crucial for radar applications.

Purpose of the Study:

To develop a computationally efficient algorithm for target parameter estimation in frequency agile radar (FAR) systems.
To address performance degradation caused by jamming environments.

Main Methods:

Suppression of barrage noise and deceptive jamming using adaptive beamforming and frequency agility.
Analytical solution for parameter estimation via low-order approximation of the multi-dimensional maximum likelihood (ML) function.

Main Results:

Effective suppression of jamming signals.
Avoidance of computationally intensive fine grid-search (FGS).
Significant reduction in computational complexity for parameter estimation.

Conclusions:

The developed algorithm enhances FAR performance in jamming environments.
The proposed method offers a computationally efficient alternative for target parameter estimation.