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

TL;DR

This paper introduces a new null broadening algorithm for navigation receivers that improves jamming suppression in dynamic environments.

Contribution

The novel algorithm uses eigenvalue sorting to broaden nulls without requiring jamming direction knowledge.

Findings

The proposed algorithm achieves 22 dB deeper nulls in jamming directions compared to CMT.

It increases signal gain by 15 dB and performs well with low jamming-to-signal ratios.

The algorithm maintains robust performance with few snapshots and directional deviations.

Abstract

Due to the narrow nulls formed by the Power Inversion (PI) algorithm, it fails to suppress jamming signals in highly dynamic scenarios effectively. This paper proposes a null broadening algorithm based on eigenvalue sorting. Unlike other algorithms, this one does not require prior knowledge of the direction of jamming. It is based on the Covariance Matrix Taper (CMT) algorithm, which orders the eigenvalues of the sampling covariance matrix. The sample covariance matrix’s eigenvalues are sorted to provide new sample data, and the rebuilt covariance matrix is then averaged forward and backward. The experimental results demonstrate that the proposed algorithm can effectively broaden the null. Compared with the CMT algorithm, the null in the jamming direction is, on average, approximately 22 dB deeper under the experimental conditions, and the gain in the direction of the sound signal is increased by around 15 dB. Moreover, the signal can be successfully acquired even when the input jamming-to-signal ratio (ISR) is relatively low. When there is a deviation in the jamming direction, the proposed algorithm demonstrates robust null broadening performance even with a small number of snapshots. The output SINR of the proposed algorithm exhibits a nearly linear relationship with the input SNR.

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