MUSIC-Aided Two-Stage Adaptive Beamforming Using Distributed Array Radar Systems

Abstract

Distributed Array Radar (DAR) systems provide enhanced spatial diversity and interference suppression capabilities compared with conventional phased-array radars. However, the presence of simultaneous mainlobe and sidelobe jammers significantly degrades target detection performance, particularly when the directions of arrival (DOAs) of interference sources are unknown. Existing two-stage adaptive beamforming approaches effectively suppress multiple jammers but generally assume prior knowledge of jammer directions, limiting their practical applicability [5]. This paper presents a MUSIC-aided two-stage adaptive beamforming framework for distributed array radar systems operating in multiple mainlobe and sidelobe jamming environments. The proposed method integrates the Multiple Signal Classification (MUSIC) algorithm to estimate the DOAs of the desired target and interference sources directly from the received array data. The estimated DOAs are subsequently employed for Stage-1 Linearly Constrained Minimum Variance (LCMV) beamforming to suppress sidelobe jammers while preserving the mainlobe response at each distributed radar node. The beamformed outputs are then transmitted to a fusion center, where Stage-2 Minimum Mean Square Error (MMSE) beamforming performs centralized mainlobe jammer suppression. MATLAB simulations demonstrate that the proposed framework accurately estimates signal directions, effectively suppresses both sidelobe and mainlobe jammers, preserves the desired target response, and improves output SINR under severe interference conditions. The proposed approach provides a practical extension to existing two-stage adaptive beamforming techniques for distributed radar systems operating in realistic electronic warfare environments.

Introduction

This study proposes a MUSIC-aided two-stage adaptive beamforming framework for Distributed Array Radar (DAR) systems to improve target detection in the presence of both mainlobe and sidelobe jammers. Distributed radar uses multiple geographically separated radar nodes to achieve higher angular resolution, better interference suppression, and greater robustness than conventional single-array radar systems.

Conventional adaptive beamforming methods such as MVDR and LCMV effectively suppress sidelobe interference but suffer from mainlobe distortion when strong jammers are located near the desired target. Existing two-stage beamforming approaches address this issue using LCMV beamforming at individual radar nodes followed by MMSE beamforming at a fusion center. However, these methods assume that the Directions of Arrival (DOAs) of the target and jammers are known beforehand, which is impractical in real-world electronic warfare scenarios.

To overcome this limitation, the proposed framework integrates the MUSIC (Multiple Signal Classification) algorithm to estimate target and jammer DOAs directly from the received radar data. The estimated DOAs are used to generate accurate steering vectors for Stage-1 LCMV beamforming at each radar node and Stage-2 MMSE beamforming at the fusion center. This enables effective suppression of both mainlobe and sidelobe interference without requiring prior knowledge of jammer locations.

The distributed radar system consists of 8 radar nodes, each equipped with a 30-element Uniform Linear Array (ULA) operating at 10 GHz with half-wavelength element spacing. The received signals include the desired target echo, multiple jammer signals, and additive white Gaussian noise. The received data are used to estimate the covariance matrix, which serves as the input for MUSIC-based DOA estimation and adaptive beamforming.

The proposed processing framework consists of three stages:

1. MUSIC-based DOA estimation using eigenvalue decomposition of the covariance matrix to identify the directions of the target and jammers.
2. Stage-1 LCMV beamforming at each radar node to suppress sidelobe jammers while maintaining the desired target response.
3. Stage-2 MMSE beamforming at the fusion center to eliminate remaining mainlobe interference and maximize the output Signal-to-Interference-plus-Noise Ratio (SINR).

The main contributions of the work include:

• Integration of MUSIC-based DOA estimation with two-stage adaptive beamforming.
• Elimination of the need for prior knowledge of jammer directions.
• Complete MATLAB implementation of the distributed radar signal processing chain.
• Validation under simultaneous mainlobe and sidelobe jamming using beam pattern and SINR performance analysis.

Conclusion

This paper presented a MUSIC-aided two-stage adaptive beamforming framework for distributed array radar systems operating under simultaneous mainlobe and sidelobe jamming conditions. Unlike conventional two-stage beamforming approaches that assume prior knowledge of jammer directions, the proposed framework integrates MUSIC-based direction-of-arrival estimation to automatically determine the spatial locations of the desired target and interference sources. The estimated steering vectors are subsequently employed for Stage-1 LCMV beamforming at individual radar nodes and Stage-2 MMSE beamforming at the fusion center. Simulation results demonstrate that the proposed framework accurately estimates the DOAs of multiple interference sources while effectively suppressing both sidelobe and mainlobe jammers. The sequential beamforming strategy preserves the desired target response, produces narrow adaptive nulls toward jammer directions, and improves the output SINR under severe electronic interference. The proposed method enhances the practical applicability of distributed array radar systems by eliminating the requirement for prior jammer direction information while maintaining robust adaptive beamforming performance. The proposed framework assumes narrowband signals and static jammer locations; future work will consider wideband signals and dynamic electronic warfare environments. Future work will investigate the extension of the proposed framework to dynamic multi-target environments with moving jammers, wideband signal models, and experimental validation using real distributed radar platforms.

References

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Copyright

Copyright © 2026 Budutha Harshitha, Dr. M. Madhavi Latha. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.