Direction finding of correlated sources in extremely low SNR environment

Document Type : Original Article

Authors

1 Department of Electronic Warfare, Military Technical College, Cairo, Egypt

2 Department of Communication, Military Technical College, Cairo, Egypt

Abstract

Direction-Finding (DF) in low Signal-to-Noise Ratio (SNR)
environments presents challenges in accurate Angle-of-Arrival (AOA)
estimation, resolution, and maximum detection range due to extrinsic
sources and hardware imperfections, particularly when dealing with
correlated sources. Spatial-Smoothing (SS) techniques are developed
to solve the problem of fully correlated sources at the expense of
potential loss of both antenna aperture and angular resolution. In this
paper, we introduce a pre-processing ”oversampling and averaging”
stage that works collaboratively with SS stage to overcome the degraded
resolution performance when dealing with correlated sources. We show
that oversampling intercepted signal, with an over-sampling ratio (OSR),
and decimating it back to its original Nyquist rate can significantly
enhance the SS-based conventional Multiple Signal Classification
(MUSIC) AOA estimator’s ability to resolve fully correlated sources
at low SNR and narrow angular separation, which carried over a lowcost
hardware environment. We derive the Cramér–Rao Bound (CRB)
for AOA estimation, which shows a reduction by the same OSR factor.
Simulation results at SNR= −15 dB with OSR=50, ensure a drastic drop
in the Root-Mean-Square-Error (RMSE) of the MUSIC-based AOA
estimator from 31◦ to 0.5◦. The resolution enhancement is confirmed
by sharper peaks in the MUSIC spectrum. Simulation analysis further
validates the improvement in the SS method. To demonstrate practical
feasibility, we implement a prototype test-bed using the National-
Instrument-Universal-Software-Radio-Peripheral (NI-USRP) Software-
Defined-Radio (SDR) platform. Experimental results confirm the
effectiveness of our proposed approach. Detailed mathematical analyses
in the appendices support the derived results and defend our findings.

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