2,523 research outputs found
Cognitive dual coprime frequency diverse array MIMO radar network for target discrimination and main-lobe interference mitigation.
The authors propose a novel dual coprime frequency diverse array (FDA) multiple input multiple output (DCFDA-MIMO) radar network design, empowered by cognitive capabilities, aimed at target discrimination and mitigation of interference present in the standalone radar systems. That is, the proposed DCFDA-MIMO design capitalises on the complementary advantages of FDAs for target discrimination and coprime arrays for enhanced resolution, resulting in superior performance. Additionally, the proposed DCFDA-MIMO network employs a 2D multiple signal classification algorithm to achieve high-resolution target localisation. By incorporating cognitive techniques based on the action-perception cycle, the proposed approach demonstrates notable improvements in multiple target detection and tracking accuracy with fewer number of antenna elements as compared to existing techniques. Furthermore, it enhances individual radar beamforming performance for interference suppression and true target detection without prior information
Optimum Design for Sparse FDA-MIMO Automotive Radar
Automotive radars usually employ multiple-input multiple-output (MIMO) antenna arrays to achieve high azimuthal resolution with fewer elements than a phased array. Despite this advantage, hardware costs and desired radar size limits the usage of more antennas in the array. Similar trade-off is encountered while attempting to achieve high range resolution which is limited by the signal bandwidth. However, nowadays given the demand for spectrum from communications services, wide bandwidth is not readily available. To address these issues, we propose a sparse variant of Frequency Diverse Array MIMO (FDA-MIMO) radar which enjoys the benefits of both FDA and MIMO techniques, including fewer elements, decoupling, and efficient joint estimation of target parameters. We then employ the Cram\'{e}r-Rao bound for angle and range estimation as a performance metric to design the optimal antenna placement and carrier frequency offsets for the transmit waveforms. Numerical experiments suggest that the performance of sparse FDA-MIMO radar is very close to the conventional FDA-MIMO despite 50\% reduction in the bandwidth and antenna elements
Fast Implementation of Transmit Beamforming for Colocated MIMO Radar
Multiple-input Multiple-output (MIMO) radars benefit from spatial and waveform diversities to improve the performance potential. Phased array radars transmit scaled versions of a single waveform thereby limiting the transmit degrees of freedom to one. However MIMO radars transmit diverse waveforms from different transmit array elements thereby increasing the degrees of freedom to form flexible transmit beampatterns. The transmit beampattern of a colocated MIMO radar depends on the zero-lag correlation matrix of different transmit waveforms. Many solutions have been developed for designing the signal correlation matrix to achieve a desired transmit beampattern based on optimization algorithms in the literature. In this paper, a fast algorithm for designing the correlation matrix of the transmit waveforms is developed that allows the next generation radars to form flexible beampatterns in real-time. An efficient method for sidelobe control with negligible increase in mainlobe width is also presented
Compressive Sensing for MIMO Radar
Multiple-input multiple-output (MIMO) radar systems have been shown to
achieve superior resolution as compared to traditional radar systems with the
same number of transmit and receive antennas. This paper considers a
distributed MIMO radar scenario, in which each transmit element is a node in a
wireless network, and investigates the use of compressive sampling for
direction-of-arrival (DOA) estimation. According to the theory of compressive
sampling, a signal that is sparse in some domain can be recovered based on far
fewer samples than required by the Nyquist sampling theorem. The DOA of targets
form a sparse vector in the angle space, and therefore, compressive sampling
can be applied for DOA estimation. The proposed approach achieves the superior
resolution of MIMO radar with far fewer samples than other approaches. This is
particularly useful in a distributed scenario, in which the results at each
receive node need to be transmitted to a fusion center for further processing
Experimental Synthetic Aperture Radar with Dynamic Metasurfaces
We investigate the use of a dynamic metasurface as the transmitting antenna
for a synthetic aperture radar (SAR) imaging system. The dynamic metasurface
consists of a one-dimensional microstrip waveguide with complementary electric
resonator (cELC) elements patterned into the upper conductor. Integrated into
each of the cELCs are two diodes that can be used to shift each cELC resonance
out of band with an applied voltage. The aperture is designed to operate at K
band frequencies (17.5 to 20.3 GHz), with a bandwidth of 2.8 GHz. We
experimentally demonstrate imaging with a fabricated metasurface aperture using
existing SAR modalities, showing image quality comparable to traditional
antennas. The agility of this aperture allows it to operate in spotlight and
stripmap SAR modes, as well as in a third modality inspired by computational
imaging strategies. We describe its operation in detail, demonstrate
high-quality imaging in both 2D and 3D, and examine various trade-offs
governing the integration of dynamic metasurfaces in future SAR imaging
platforms
Computational polarimetric microwave imaging
We propose a polarimetric microwave imaging technique that exploits recent
advances in computational imaging. We utilize a frequency-diverse cavity-backed
metasurface, allowing us to demonstrate high-resolution polarimetric imaging
using a single transceiver and frequency sweep over the operational microwave
bandwidth. The frequency-diverse metasurface imager greatly simplifies the
system architecture compared with active arrays and other conventional
microwave imaging approaches. We further develop the theoretical framework for
computational polarimetric imaging and validate the approach experimentally
using a multi-modal leaky cavity. The scalar approximation for the interaction
between the radiated waves and the target---often applied in microwave
computational imaging schemes---is thus extended to retrieve the susceptibility
tensors, and hence providing additional information about the targets.
Computational polarimetry has relevance for existing systems in the field that
extract polarimetric imagery, and particular for ground observation. A growing
number of short-range microwave imaging applications can also notably benefit
from computational polarimetry, particularly for imaging objects that are
difficult to reconstruct when assuming scalar estimations.Comment: 17 pages, 15 figure
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