Microwave Linear Analog Computer (MiLAC)-Aided MIMO Radar Sensing: Transmit Beamforming Design and DoA Estimation

2026-05-20

AI Summary

This work addresses the high hardware cost, power consumption, and digital processing complexity inherent in massive MIMO radar systems. It pioneers the integration of microwave linear analog computing (MiLAC) into radar sensing, migrating both transmit beamforming and two-dimensional DFT-based direction-of-arrival (DoA) estimation to the analog domain. Under reciprocity constraints, the proposed approach achieves the same Cramér–Rao bound (CRB) as fully digital systems. By formulating a weighted CRB minimization problem and solving it via a penalty dual decomposition (PDD) algorithm—combined with low-resolution DACs and an architecture that eliminates RF chains and ADCs—the method significantly reduces hardware overhead and power consumption while maintaining equivalent DoA estimation accuracy and entirely avoiding digital DFT computations.

Abstract

Multiple-input multiple-output (MIMO) radar has waveform diversity and large spatial degrees of freedom (DoFs), making it attractive for high-resolution sensing. Scaling MIMO radar to massive arrays can further improve sensing performance, but it also increases hardware cost, power consumption, and digital processing complexity. The microwave linear analog computer (MiLAC) can tackle these challenges by moving linear operations from the digital domain to the analog domain. MiLAC has shown promising benefits for communications in recent studies and this paper identifies its potential for radar sensing. Specifically, we consider both MiLAC-aided transmit beamforming and receiver-side two-dimensional discrete Fourier transform (2D-DFT)-based direction-of-arrival (DoA) estimation. For transmit beamforming, we formulate a weighted Cramer Rao bound (CRB) minimization problem under lossless and reciprocal MiLAC constraints and propose a penalty dual decomposition (PDD)-based iterative algorithm to address the non-convex problem. We further prove that MiLAC-aided and fully-digital beamforming achieve the same CRB. For receiver processing, we show that the 2D DFT can be implemented by a lossless reciprocal MiLAC, which enables analog-domain DoA estimation without digital optimization. Numerical results confirm the theoretical finding and show that the MiLAC-aided approach achieves the same CRB and DoA estimation performance as the fully-digital benchmark. Meanwhile, hardware cost and power consumption are reduced because only low-resolution DACs are required at the transmitter, while RF chains and ADCs are eliminated at the receiver. Moreover, performing the 2D DFT in the analog domain eliminates all digital DFT operations for DoA estimation.

Problem

• MIMO radar
• hardware cost
• power consumption
• digital processing complexity
• massive arrays

Innovation

• Microwave Linear Analog Computer
• MIMO Radar
• Analog Signal Processing
• Beamforming
• Direction-of-Arrival Estimation