Random Multiplexing for an MIMO-OFDM Radar With Compressed Sensing-Based Reconstruction

Abstract

In many applications, the direction of arrival (DoA) information of the radar signal plays a decisive role in target localization. A multiple-input multiple-output (MIMO) radar allows to obtain the position of an object in space within one measurement frame. Recent research and publications verify the high potential of digital radar principles such as orthogonal frequency-division multiplexing (OFDM). In this letter, a MIMO-OFDM approach based on random frequency and time-division multiplexing is presented. It is enhanced by a multidimensional compressed sensing method which utilizes the information of multiple channels. The approach is validated and compared to other MIMO-OFDM approaches using measurements of an experimental radar at 72.5 GHz.

Soft computing based compressive sensing techniques in signal processing: A comprehensive review

“…To provide optimal performance, CS techniques incorporate random structures, and they are widely applied to restore the missing signal samples. Christina Knill et al [55] used CS post-processing for enhancing the Multi-Input Multi-Output (MIMO) approach, which leads to an effective regain of the full processing of the state-of-art Orthogonal Frequency-Division Multiplexing (OFDM). This method makes benefits for the information present in the CS, which leads to effective reconstruction, accelerated processing, and low computational complexity.…”

Compressive Sensing-Based Radar Imaging and Subcarrier Allocation for Joint MIMO OFDM Radar and Communication System

“…However, in the MUSIC algorithm, the estimation of covariance matrix of the received signal is required, which may incur a considerable latency to collect multiple OFDM waveforms at the multiple receive (Rx) antennas. In [ 11 , 12 , 13 ], compressed sensing-based radar imaging algorithms have been developed for multiple-input multiple-output (MIMO) OFDM radar, not requiring the subspace estimation. However, they do not consider the communication performance when multiple antennas are deployed at RadCom platform.…”

“…Specifically, to get 2D radar images with high resolution, a compressive sensing-based imaging algorithm is first proposed when the subcarriers are orthogonally allocated across multiple transmit (Tx) antennas. Differently from the compressive sensing based estimation for MIMO OFDM radars in [ 11 , 12 , 13 ], where basis pursuit (BP) or orthogonal matching pursuit (OMP) algorithms are exploited, the Bayesian matching pursuit (BMP)-based imaging method (which is successfully applied to the FMCW MIMO radar system [ 14 ]) is developed for the MIMO OFDM RadCom platform. Because both the mean square error (MSE) of the radar image and the achievable rate are affected by the subcarrier allocation across multiple Tx antennas.…”

OFDM Radar Range Accuracy Enhancement Using Fractional Fourier Transformation and Phase Analysis Techniques

“…OFDM radar, with its multi-carrier parallel transmission and reception characteristics, has attracted wide attention in high-precision rang. Random OFDM pair radar based on compressive sensing with high resolution range reconstruction was proposed in [17], [18]. Meanwhile, the adaptive OFDM radar waveform design methods were proposed for improved micro-doppler estimation in [19], [20].…”