25 research outputs found
A novel approach to robust radar detection of range-spread targets
This paper proposes a novel approach to robust radar detection of
range-spread targets embedded in Gaussian noise with unknown covariance matrix.
The idea is to model the useful target echo in each range cell as the sum of a
coherent signal plus a random component that makes the signal-plus-noise
hypothesis more plausible in presence of mismatches. Moreover, an unknown power
of the random components, to be estimated from the observables, is inserted to
optimize the performance when the mismatch is absent. The generalized
likelihood ratio test (GLRT) for the problem at hand is considered. In
addition, a new parametric detector that encompasses the GLRT as a special case
is also introduced and assessed. The performance assessment shows the
effectiveness of the idea also in comparison to natural competitors.Comment: 28 pages, 8 figure
Design of Customized Adaptive Radar Detectors in the CFAR Feature Plane
The paper addresses the design of adaptive radar detectors with desired behavior, in Gaussian disturbance with unknown statistics. Specifically, based on detection probability specifications for chosen signal-to-noise ratios and steering vector mismatch levels, a methodology for the design of customized constant false alarm rate (CFAR) detectors is devised in a suitable feature plane obtained from two maximal invariant statistics. To overcome the analytical and numerical intractability of the resulting optimization problem, a novel general reduced-complexity algorithm is developed, which is shown to be effective in providing a feasible solution (i.e., fulfilling a constraint on the probability of false alarm) while controlling the behavior under both matched and mismatched conditions, so enabling the design of fully customized adaptive CFAR detectors
Downlink Single-Snapshot Localization and Mapping with a Single-Antenna Receiver
5G mmWave MIMO systems enable accurate estimation of the user position and
mapping of the radio environment using a single snapshot when both the base
station (BS) and user are equipped with large antenna arrays. However, massive
arrays are initially expected only at the BS side, likely leaving users with
one or very few antennas. In this paper, we propose a novel method for
single-snapshot localization and mapping in the more challenging case of a user
equipped with a single-antenna receiver. The joint maximum likelihood (ML)
estimation problem is formulated and its solution formally derived. To avoid
the burden of a full-dimensional search over the space of the unknown
parameters, we present a novel practical approach that exploits the sparsity of
mmWave channels to compute an approximate joint ML estimate. A thorough
analysis, including the derivation of the Cram\'er-Rao lower bounds, reveals
that accurate localization and mapping can be achieved also in a MISO setup
even when the direct line-of-sight path between the BS and the user is severely
attenuated
Low-Complexity Accurate Mmwave Positioning for Single-Antenna Users Based on Angle-of-Departure and Adaptive Beamforming
The problem of position estimation of a mobile user equipped with a single antenna receiver using downlink transmissions is addressed. The advantages of this setup compared to the classical MIMO and uplink scenarios are analyzed in terms of achievable theoretical performance (Cram\ue9r-Rao bounds) considering a realistic power budget. Based on this analysis, a low-complexity two-step algorithm with improved localization performance is proposed, which first performs a (coarse) angle of departure estimation and then precodes the down-link signal to introduce beamforming towards the user direction. Results demonstrate that position estimation in downlink can be potentially much more accurate than in uplink, even in presence of multiple users in the system
Low-Complexity Downlink Channel Estimation in mmWave Multiple-Input Single-Output Systems
This paper tackles the problem of channel estimation in mmWave multiple-input single-output systems, where users are equipped with single-antenna receivers. By leveraging broadcast transmissions in the downlink channel, two novel low-complexity estimation approaches are devised, able to operate even in presence of a reduced number of transmit antennas or limited bandwidth. Numerical results show that the proposed algorithms provide accurate estimates of the channel parameters, achieving at the same time about 50% complexity reduction compared to existing approaches
Millimeter-Wave Downlink Positioning with a Single-Antenna Receiver
The paper addresses the problem of determining the unknown position of a mobile station for a mmWave MISO system. This setup is motivated by the fact that massive arrays will be initially implemented only on 5G base stations, likely leaving mobile stations with one antenna. The maximum likelihood solution to this problem is devised based on the time of flight and angle of departure of received downlink signals. While positioning in the uplink would rely on angle of arrival, it presents scalability limitations that are avoided in the downlink. To circumvent the multidimensional optimization of the optimal joint estimator, we propose two novel approaches amenable to practical implementation thanks to their reduced complexity. A thorough analysis, which includes the derivation of relevant Cram\ue9r-Rao lower bounds, shows that it is possible to achieve quasi-optimal performance even in presence of few transmissions, low SNRs, and multipath propagation effects
RIS-Aided Monostatic Sensing and Object Detection with Single and Double Bounce Multipath
We propose a framework for monostatic sensing by a user equipment (UE), aided
by a reconfigurable intelligent surface (RIS) in environments with single- and
double-bounce signal propagation. We design appropriate UE-side precoding and
combining, to facilitate signal separation. We derive the adaptive detection
probabilities of the resolvable signals, based on the geometric channel
parameters of the links. Then, we estimate the passive objects using both the
double-bounce signals via passive RIS (i.e., RIS-sensing) and the single-bounce
multipath direct to the objects (i.e., non-RIS-sensing), based on a mapping
filter. Finally, we provide numerical results to demonstrate that effective
sensing can be achieved through the proposed framework