# Multi-target Range and Angle detection for MIMO-FMCW radar with limited antennas

## TL;DR

This paper introduces a novel multi-target localization algorithm for MIMO FMCW radar with sparse arrays, combining DFT-based focusing and compressive sensing to improve detection accuracy and reduce hardware complexity.

## Contribution

It presents a new method for multi-target detection in MIMO FMCW radar using sparse arrays, integrating DFT focusing and CS techniques for enhanced performance.

## Key findings

- Effective detection of multiple targets with sparse arrays
- Outperforms classical methods in simulation
- Reduces hardware and computational complexity

## Abstract

Multiple-input multiple-output (MIMO) radar has several advantages with respect to the traditional radar array systems in terms of performance and flexibility. However, in order to achieve high angular resolution, a MIMO radar requires a large number of transmit and receive antennas, which increases hardware design and computational complexities. Although spatial compressive sensing (CS) has been recently considered for a pulsed-waveform MIMO radar with sparse random arrays, such methods for the frequency-modulated continuous wave (FMCW) radar remain largely unexplored. In this context, we propose a novel multi-target localization algorithm in the range-angle domain for a MIMO FMCW radar with a sparse array of randomly placed transmit and receive elements. In particular, we first obtain the targets' range-delays using a discrete Fourier transform (DFT)-based focusing operation. The target angles are then recovered at each detected range using CS-based techniques exploiting the sparsity of the target scene. Our simulation results demonstrate the effectiveness of the proposed algorithm over the classical methods in detecting multiple targets with a sparse array.