2,023 research outputs found
Target localization in MIMO radar systems
MIMO (Multiple-Input Multiple-Output) radar systems employ multiple antennas to transmit multiple waveforms and engage in joint processing of the received echoes from the target. MIMO radar has been receiving increasing attention in recent years from researchers, practitioners, and funding agencies. Elements of MIMO radar have the ability to transmit diverse waveforms ranging from independent to fully correlated. MIMO radar offers a new paradigm for signal processing research. In this dissertation, target localization accuracy performance, attainable by the use of MIMO radar systems, configured with multiple transmit and receive sensors, widely distributed over an area, are studied. The Cramer-Rao lower bound (CRLB) for target localization accuracy is developed for both coherent and noncoherent processing. The CRLB is shown to be inversely proportional to the signal effective bandwidth in the noncoherent case, but is approximately inversely proportional to the carrier frequency in the coherent case. It is shown that optimization over the sensors\u27 positions lowers the CRLB by a factor equal to the product of the number of transmitting and receiving sensors. The best linear unbiased estimator (BLUE) is derived for the MIMO target localization problem. The BLUE\u27s utility is in providing a closed-form localization estimate that facilitates the analysis of the relations between sensors locations, target location, and localization accuracy. Geometric dilution of precision (GDOP) contours are used to map the relative performance accuracy for a given layout of radars over a given geographic area. Coherent processing advantage for target localization relies on time and phase synchronization between transmitting and receiving radars. An analysis of the sensitivity of the localization performance with respect to the variance of phase synchronization error is provided by deriving the hybrid CRLB. The single target case is extended to the evaluation of multiple target localization performance. Thus far, the analysis assumes a stationary target. Study of moving target tracking capabilities is offered through the use of the Bayesian CRLB for the estimation of both target location and velocity. Centralized and decentralized tracking algorithms, inherit to distributed MIMO radar architecture, are proposed and evaluated. It is shown that communication requirements and processing load may be reduced at a relatively low performance cost
Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays
Massive MIMO (multiple-input multiple-output) is no longer a "wild" or
"promising" concept for future cellular networks - in 2018 it became a reality.
Base stations (BSs) with 64 fully digital transceiver chains were commercially
deployed in several countries, the key ingredients of Massive MIMO have made it
into the 5G standard, the signal processing methods required to achieve
unprecedented spectral efficiency have been developed, and the limitation due
to pilot contamination has been resolved. Even the development of fully digital
Massive MIMO arrays for mmWave frequencies - once viewed prohibitively
complicated and costly - is well underway. In a few years, Massive MIMO with
fully digital transceivers will be a mainstream feature at both sub-6 GHz and
mmWave frequencies. In this paper, we explain how the first chapter of the
Massive MIMO research saga has come to an end, while the story has just begun.
The coming wide-scale deployment of BSs with massive antenna arrays opens the
door to a brand new world where spatial processing capabilities are
omnipresent. In addition to mobile broadband services, the antennas can be used
for other communication applications, such as low-power machine-type or
ultra-reliable communications, as well as non-communication applications such
as radar, sensing and positioning. We outline five new Massive MIMO related
research directions: Extremely large aperture arrays, Holographic Massive MIMO,
Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive
MIMO.Comment: 20 pages, 9 figures, submitted to Digital Signal Processin
Target Localization Accuracy Gain in MIMO Radar Based Systems
This paper presents an analysis of target localization accuracy, attainable
by the use of MIMO (Multiple-Input Multiple-Output) radar systems, configured
with multiple transmit and receive sensors, widely distributed over a given
area. The Cramer-Rao lower bound (CRLB) for target localization accuracy is
developed for both coherent and non-coherent processing. Coherent processing
requires a common phase reference for all transmit and receive sensors. The
CRLB is shown to be inversely proportional to the signal effective bandwidth in
the non-coherent case, but is approximately inversely proportional to the
carrier frequency in the coherent case. We further prove that optimization over
the sensors' positions lowers the CRLB by a factor equal to the product of the
number of transmitting and receiving sensors. The best linear unbiased
estimator (BLUE) is derived for the MIMO target localization problem. The
BLUE's utility is in providing a closed form localization estimate that
facilitates the analysis of the relations between sensors locations, target
location, and localization accuracy. Geometric dilution of precision (GDOP)
contours are used to map the relative performance accuracy for a given layout
of radars over a given geographic area.Comment: 36 pages, 5 figures, submitted to IEEE Transaction on Information
Theor
MEDUSA: Scalable Biometric Sensing in the Wild through Distributed MIMO Radars
Radar-based techniques for detecting vital signs have shown promise for
continuous contactless vital sign sensing and healthcare applications. However,
real-world indoor environments face significant challenges for existing vital
sign monitoring systems. These include signal blockage in non-line-of-sight
(NLOS) situations, movement of human subjects, and alterations in location and
orientation. Additionally, these existing systems failed to address the
challenge of tracking multiple targets simultaneously. To overcome these
challenges, we present MEDUSA, a novel coherent ultra-wideband (UWB) based
distributed multiple-input multiple-output (MIMO) radar system, especially it
allows users to customize and disperse the into sub-arrays.
MEDUSA takes advantage of the diversity benefits of distributed yet wirelessly
synchronized MIMO arrays to enable robust vital sign monitoring in real-world
and daily living environments where human targets are moving and surrounded by
obstacles. We've developed a scalable, self-supervised contrastive learning
model which integrates seamlessly with our hardware platform. Each attention
weight within the model corresponds to a specific antenna pair of Tx and Rx.
The model proficiently recovers accurate vital sign waveforms by decomposing
and correlating the mixed received signals, including comprising human motion,
mobility, noise, and vital signs. Through extensive evaluations involving 21
participants and over 200 hours of collected data (3.75 TB in total, with 1.89
TB for static subjects and 1.86 TB for moving subjects), MEDUSA's performance
has been validated, showing an average gain of 20% compared to existing systems
employing COTS radar sensors. This demonstrates MEDUSA's spatial diversity gain
for real-world vital sign monitoring, encompassing target and environmental
dynamics in familiar and unfamiliar indoor environments.Comment: Preprint. Under Revie
Robust Design of Transmit Waveform and Receive Filter For Colocated MIMO Radar
We consider the problem of angle-robust joint transmit waveform and receive
filter design for colocated Multiple-Input Multiple-Output (MIMO) radar, in the
presence of signal-dependent interferences. The design problem is cast as a
max-min optimization problem to maximize the worst-case output
signal-to-interference-plus-noise-ratio (SINR) with respect to the unknown
angle of the target of interest. Based on rank-one relaxation and semi-definite
programming (SDP) representation of a nonnegative trigonometric polynomial, a
cyclic optimization algorithm is proposed to tackle this problem. The
effectiveness of the proposed method is illustrated via numerical examples.Comment: 6 pages, 13 figures, part of this work was submitted to IEEE Signal
Processing Letters; (short introduction; typos corrected; revised statement
in section III-B and IV; revised figure labels
- …