Compressive-Sensing-Aided MIMO Radar Enabling Multi-Functional and Compact Sensors in Air Scenarios Using Optimized Antenna Arrays

Abstract

We address the problem of direction-of-arrival (DoA) estimation for air targets using a compact, multi-functional radar sensor. In order to enhance the angular resolution of such sensors while exploiting the sparseness of typical air scenarios, we consider the combination of a multipleinput multiple-output (MIMO) radar approach with suitable compressive-sensing (CS) techniques. In particular, we investigate the combination of MIMO processing for two-dimensional (2D) antenna arrays with CS-based angular processing for three-dimensional (3D) target localization and 2D DoA estimation in azimuth and elevation. We analyze the benefits of randomized antenna element positions in one and two dimensions and devise optimized array geometries for practicable aperture sizes. In particular, we take physical side constraints into account, such as the smallest realizable/ desired element spacing as well as area restrictions for antenna placement within the overall aperture. The aperture area reserved by this means could be used to accommodate additional hardware components enabling a multi-functional sensor approach. Extensive computer simulation results for different 3D target scenarios illustrate the advantages of our CS-based compact MIMO radar approach with randomized antenna elements compared to fully randomized arrays, given the same number of physical antenna elements.

Localization as a key enabler of 6G wireless systems: A comprehensive survey and an outlook

“…Local trust zones [225]- [230] Mapping [231] Massive twinning [232] Indoor [231] Outdoor [228]- [230] mmWave/THz [226], [228], [229] Sensors [225], [227], [229]- [231] Beamforming [226] Radar [228] , VLP [232] RSS, TDoA, DoA, RMSE…”

“…Furthermore, Salari et al, in [227], documented a CSbased approach for obtaining TDOA estimates, which requires a limited number of signal samples, leading to a significantly lower computational complexity. A CS-aided MIMO scheme for aerial scenarios was reported in [228]. In more detail, the authors combined MIMO techniques for 2D antenna arrays and CS-based DoA estimation for 3D target tracking.…”

Localization as a Key Enabler of 6G Wireless Systems: A Comprehensive Survey and an Outlook

“…Local trust zones [257]- [262] Mapping [263] Massive twinning [264] Indoor [263] Outdoor [260]- [262] mmWave/THz [258], [260], [261] Sensors [257], [259], [261]- [263] Radar [260], VLP [264] Beamforming [258] RSS, TDoA, DoA, RMSE Multidimensional Scaling Robots [265] Local trust zones [266]- [274] Massive twinning [275] Indoor [265], [272], [275] Outdoor [267]- [269], [271], [274] Non-terrestrial [270], [273] Sensors [265]- [275] ToF, TDoA, RSS, RMSE…”

“…Furthermore, Salari et al, in [259], documented a CS-based approach for obtaining TDOA estimates, which requires a limited number of signal samples, leading to a significantly lower computational complexity. A CS-aided MIMO scheme for aerial scenarios was reported in [260]. In more detail, the authors combined MIMO techniques for 2D antenna arrays and CS-based DoA estimation for 3D target tracking.…”

Improving estimation performance of compressive sensing‐based multiple‐input multiple‐output radar using electronic beamsteering

“…A promising way to use fewer samples while achieving high accuracy is provided by the concept of compressive sensing (CS), which is a class of techniques able to solve underdetermined systems of linear equations with sparse inputs [26][27][28]. CS has been applied to many different domains in radar signal processing [29], for example, to reconstruct sparse signals with sampling rates far below the Nyquist-rate [30,31], for DoA estimation [32,33], array design [34], and extended target detection [35]. In terms of hardware design, it has been shown that CS performs well with pseudo-random array topologies [34,36] and also with widely separated antenna arrays with inter-element spacing d > λ [37,38].…”