A Study of Null Broadening Algorithms for Navigation Receivers in Highly Dynamic Scenarios

Area of Science:

• Signal Processing
• Adaptive Filtering
• Array Signal Processing

Background:

• Traditional Power Inversion (PI) algorithms exhibit narrow nulls, limiting jamming suppression effectiveness in dynamic scenarios.
• Existing methods often require prior knowledge of jamming signal direction, restricting their applicability.
• Effective jamming signal suppression is crucial for maintaining signal integrity in complex electromagnetic environments.

Purpose of the Study:

• To propose and evaluate a novel null broadening algorithm for enhanced jamming signal suppression.
• To develop a method that does not require prior knowledge of the jamming signal's direction.
• To improve the performance of adaptive beamforming in dynamic and challenging signal environments.

Main Methods:

• The proposed algorithm utilizes eigenvalue sorting of the sampling covariance matrix.
• It builds upon the Covariance Matrix Taper (CMT) algorithm by reordering eigenvalues.
• The method involves averaging the rebuilt covariance matrix both forward and backward.

Main Results:

• The algorithm effectively broadens nulls, outperforming the CMT algorithm.
• Null depth in the jamming direction is increased by approximately 22 dB, and signal gain by 15 dB.
• Successful signal acquisition is achieved even at low jamming-to-signal ratios (ISR) and with few snapshots.

Conclusions:

• The proposed eigenvalue sorting null broadening algorithm offers superior jamming suppression compared to existing methods.
• It demonstrates robustness and effectiveness in dynamic scenarios without requiring jammer direction information.
• The algorithm provides a significant improvement in signal-to-interference-plus-noise ratio (SINR) performance.