Compressive sensing MTI processing in distributed MIMO radars

Abstract

It is shown that the detection performance can be significantly improved using the recent technology of multiple-input multiple-output (MIMO) radar systems. This is a result of the spatial diversity in such systems due to the viewing of the target from different angles. On the other hand, the moving target indication (MTI) processing has long been known and applied in the traditional pulse radars to detect weak moving targets in the presence of strong clutter signals. The authors propose a procedure based on the compressive sensing idea, in order to apply the MTI processing in a MIMO radar with widely separated antennas. Although a clutter is included in the signal model and a different radar cross-section value for each transmitterreceiver pair is considered which makes the problem more complex, the complexity dimension is preserved as low as possible by converting the block sparse problem into a regular sparse problem.

Compressed-Domain Detection and Estimation for Colocated MIMO Radar

“…Furthermore, we compare the proposed algorithm with one of the state-of-the-art CS recovery-based algorithms, NESTA [55], in terms of ROC, estimation accuracy, and execution time in various scenarios. NESTA is chosen, because it is a fast and accurate sparse recovery algorithm and is shown to perform well on the problem of signal reconstruction in the MIMO radar [3]. For the multitarget case, the OMP algorithm is also compared with the proposed algorithm.…”

“…Based on antennas distances, a MIMO radar is categorized into widely separated and colocated. Large distances among antennas in a widely separated MIMO radar cause different transmitterreceiver pairs to look at a target from different angles; this provides spatial diversity and results in high-resolution target localization and enhanced target detection and estimation [1]- [3]. In the colocated MIMO radar, exploiting waveform diversity results in flexible beampattern design and improved angular resolution [4]- [7].…”

Antenna Placement in a Compressive Sensing-Based Colocated MIMO Radar

“…To analyze the achieved coherence values, for each value of M, we take 2000 independent feasible samples (2000 independent placements) from the obtained probability distributions. Note that some samples are not feasible since they do not satisfy (12) or (19). We also produce 2000 independent placements by choosing the positions uniformly at random in the interval [0, L t ] (the placement method proposed by [28] and [29]).…”

“…An improved angular resolution, parameter identifiability, and interference rejection are the main advantages of colocated MIMO radar. In recent years, the application of compressive sensing (CS) to MIMO radar has been investigated both in the colocated case [6]- [9] and the widely separated case [10]- [12], either to reduce the overall cost and complexity (e.g., by reducing the number of TX/RX elements) or to improve the performance under the same number of antennas and measurements. In this article, by focusing on CS-based colocated MIMO radar, we investigate the placement of TX and RX antennas in linear arrays to improve the CS-based target detection and estimation performance.…”

A Prediction-Based Spatial-Spectral Adaptive Hyperspectral Compressive Sensing Algorithm

“…In recent years, they have been widely used in agriculture and forestry research, marine monitoring, natural disaster monitoring, and military reconnaissance [ 1 ]. However, with the increasing development of remote sensing technology, the requirement to increase the resolution of hyperspectral data has led to an extreme increase in its amount, which has caused tremendous pressure on the transmission and storage of hyperspectral images [ 2 , 3 ]. Solving this problem can start from the hardware itself, such as increasing the storage space of the hardware.…”