An Implementation Scheme of Range and Angular Measurements for FMCW MIMO Radar via Sparse Spectrum Fitting

Abstract

The work presented in this paper is about implementing a frequency-modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) positioning radar and a sparse spectrum fitting (SpSF) algorithm for range and angular measurements. First, we designed a coherent FMCW MIMO radar system working in the S-band with low power consumption that consists of four transmitter and four receiver antennas and has the ability to extend its virtual aperture; thus, this system can achieve a higher resolution than conventional phased array radars. Then, the SpSF algorithm was designed for estimating the distance and angle of the targets in the FMCW MIMO radar. Due to the fact that the SpSF algorithm can exploit the spatial sparsity diversity of a signal, the SpSF algorithm that is applied in the designed MIMO radar system can achieve a better estimation performance than the multiple signal classification (MUSIC) and Capon algorithms, especially in the context of small snapshots and low signal-to-noise ratios (SNRs). The simulated and experimental results are used to prove the effectiveness of the designed MIMO radar and the superior performance of the algorithm.

A SISO FMCW radar based on inherently frequency scanning antennas for 2-D indoor tracking of multiple subjects

“…Single-input and single-output (SISO) solutions, using only one (omnidirectional or directive) antenna for the transmission and one for the reception, are generally unable to resolve targets in the angular dimension. To solve this issue, a typically adopted solution is to consider a multiple-input and multiple-output (MIMO) UWB architecture, which provides, at the same time, a good range resolution and an angular separation 35 , 45 – 47 . The main drawback of this solution is in the complexity of the hardware architecture that requires an array of antennas and multiple channels in transmission and reception, hence demanding a larger silicon area and a higher power consumption.…”

Fast Joint DOA, Range, and Velocity Estimation Method for FMCW MIMO Radar via PARAFAC

“…However, with the recent development of hardware technology, the data dimension of high-performance FMCW MIMO radar represented by Texas Instruments (TI) cascade FMCW MIMO radar has increased dramatically, and the existing super-resolution algorithms based on subspace [9], [10] or sparse representation [11], [12] are unable to cope with the greatly increased operation complexity. Meanwhile, for such scenarios, the traditional fast Fourier transform (FFT) [6] method can meet the system demand for real-time, however, its accuracy is limited by the system bandwidth and Rayleigh limit, reflecting its inability to meet the requirements of autonomous driving technology for environment sensing.…”