Sparse Multiple-input Multiple-output Array Research Articles

Enhanced Automotive Sensing Assisted by Joint Communication and Cognitive Sparse MIMO Radar

Automotive radar is mandated to provide high accuracy direction of arrival (DOA) estimation for safe driving, while remaining a low cost device for feasible mass production. Sparse multiple input multiple output (MIMO) arrays emerge as a primary candidate to meet these requirements. As DOA estimation accuracy is a main indicator of tracking performance, Cramer Rao Bound (CRB) is chosen as the goodness measurement for sparse MIMO array optimization, but its application requires prior information of the road environment. We propose a cognitive sparse MIMO array automotive radar, which “perceives” the road environment via automotive sensing supplemented by co-existed communication from roadside unit to vehicle (R2V). This information is used for co-designing a sparse MIMO array for enhanced automotive sensing and vehicle to roadside unit (V2R) communication. Note that both static RSU and dynamic RSU are usually deployed in Internet of Vehicles (IoV), which can provide continuous transmission coverage and permanent connectivity. The bi-directional communications are integrated into the automotive radar. This is achieved by joint transmit waveform design with spectral nulls for communications and with shared sparse MIMO array co-design for both sensing and high quality V2R communications. Simulation results validate the enhanced automotive sensing performance assisted by the integrated bi-directional communications in the cycle of cognitive-driven optimization.

Efficient Beamforming Technique Based on Sparse MIMO Array and Spatial Filter Bank

Spatial diversity gained through different beamforming techniques in antenna array is one of the main advantages over the single antenna. Antenna complexity and beamwidth broadening at the scanning angles other than broadside are downsides of antenna arrays that prevent its practical implementation from some applications. In this letter, we propose an efficient beamforming method based on the sparse multiple-input–multiple-output (MIMO) array and spatial filter bank (SFB). The sparse MIMO array produces narrow beams while significantly reduces the number of array elements without compromising system performance. Multiple main beams are chosen from grating lobes by SFB. By combining sparse array geometry and SFB, we can achieve a narrower beamwidth with less antenna elements, multiple beams, and constant beamwidth. The burden of system will be transferred from the frontend hardware to the signal processing back end.

Near-Field Synthetic Spectrum Imaging Algorithm Based on Single-Frequency Sparse MIMO Array

This letter presents a two-dimensional near-field synthetic spectrum imaging algorithm based on a single-frequency sparse multiple-input–multiple-output (MIMO) array. By using an especially defined backward spreading function, the proposed algorithm migrates the scattered wave field under different illuminations. A synthetic spectrum can be obtained by synthesizing the spectrums of these migration results and then interpreted to the final reconstruction. The migration processing enables the algorithm to adapt to arbitrary apertures. Meanwhile, the influences of the limited aperture, the spreading loss, and the wavefront curvature in the near field are considered and compensated. High processing speed is achieved by using a large interpixel spacing during the migration processing and by the fast Fourier transform in the frequency-wavenumber (f-w) domain. Simulations and experiments are carried out, and the results demonstrate both the high-computational efficiency and the high flexibility for arbitrary sparse MIMO array apertures.

Microwave Imaging of Non-Rigid Moving Target Using 2D Sparse MIMO Array

This paper develops a microwave/mmw (millimeter-wave) imaging method for moving objects of non-rigid body which can be used in the heavy traffic scenarios. This method is proposed with a 2D sparse MIMO (multiple-input multiple-output) array composed of two orthogonal linear arrays. In comparison to the RX antennas, a smaller number of TX antennas is used to decrease the data acquisition time of this system when using TDM (time division multiplexing) signal transmitting technique. The 2D sparse MIMO array is deployed with wide-band signals transmitted to reconstruct the backscattering coefficient in 3D space using synthetic aperture imaging technique. Then a series of synthesized 3D snapshot images are generated during the motion of the target. The non-rigid human body target is decomposed into 25 joints tracked by an optical device which is Kinect in this paper. After the decomposition, the movement of each joint can be described as a space-time trajectory tracked by Kinect, providing a preliminary indication of the 3D position of each joint. With the indication, we divide every microwave/mmw image into 25 sub-images corresponding to the joints’ position to form an image sequence of each joint. Subsequently, a segmental joint estimation method is proposed to estimate the accurate motion parameters of each joint from the image sequence. The decorrelation of the images in large angle observation can be significantly reduced by the segmental estimation which leads global optimized motion parameters. Consequently, the estimated parameters are taken into the effective movement compensation of each component. Meanwhile, the image refocusing method is presented to generate the well-focused image of each component. Finally, all the images of components are fused into one image of the whole target to reconstruct the 3D high-resolution image. Imaging simulations and measuring data experiments show that this method is efficient.

Off-the-Grid Sparse Imaging by One-Dimensional Sparse MIMO Array

Conventional multiple-input multiple-output (MIMO) technique applied into the millimeter-wave and terahertz (THz) imaging applications would suffer from the large number of array elements due to their short wavelengths. In this paper, to reduce the array elements for azimuth-range imaging, a 1-D sparse MIMO array is introduced that combines with the wideband emitted signal to achieve 2-D imaging in the near field. Provided with these greatly reduced spatial samples measured by the sparse array, an off-the-grid sparse imaging algorithm is proposed to recover the arbitrarily distributed scatterers in a 2-D plane. In particular, the proposed approach takes advantages of the MIMO geometry and matrix pencil (MP) method. It utilizes the echo in wave-number domain which is featured by the MIMO geometry to make a lossless dimension reduction from the 2-D unknown position of each scatterer into a local 1-D frequency. After estimating those local 1-D frequencies by the MP method, an MIMO-structure-determined filter is developed to fulfill the inverse mapping and finally achieve imaging without the pairing problem. Simulations and experiments verify the effectiveness of the proposed approach.