620 research outputs found
An ABORT-like detector with improved mismatched signals rejection capabilities
In this paper, we present a GLRT-based adaptive detection algorithm for extended targets with improved rejection capabilities of mismatched signals. We assume that a set of secondary data is available and that noise returns in primary and secondary data share the same statistical characterization. To increase the selectivity of the detector, similarly to the ABORT formulation, we modify the hypothesis testing problem at hand introducing fictitious signals under the null hypothesis. Such unwanted signals are supposed to be orthogonal to the nominal steering vector in the whitened observation space. The performance assessment, carried out by Monte Carlo simulation, shows that the proposed dectector ensures better rejection capabilities of mismatched signals than existing ones, at the price of a certain loss in terms of detection of matched signals
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
Cramér-Rao Bound Optimization for Joint Radar-Communication Beamforming
In this paper, we propose multi-input multi-output (MIMO) beamforming designs towards joint radar sensing and multi-user communications. We employ the Cramr-Rao bound (CRB) as a performance metric of target estimation, under both point and extended target scenarios. We then propose minimizing the CRB of radar sensing while guaranteeing a pre-defined level of signal-to-interference-plus-noise ratio (SINR) for each communication user. For the single-user scenario, we derive a closed form for the optimal solution for both cases of point and extended targets. For the multi-user scenario, we show that both problems can be relaxed into semidefinite programming by using the semidefinite relaxation approach, and prove that the global optimum can always be obtained. Finally, we demonstrate numerically that the globally optimal solutions are reachable via the proposed methods, which provide significant gains in target estimation performance over state-of-the-art benchmarks
Multi-user spatial diversity techniques for wireless communication systems
Multiple antennas at the transmitter and receiver, formally known as multiple-input
multiple-output (MIMO) systems have the potential to either increase the data rates
through spatial multiplexing or enhance the quality of services through exploitation
of diversity. In this thesis, the problem of downlink spatial multiplexing, where a
base station (BS) serves multiple users simultaneously in the same frequency band is
addressed. Spatial multiplexing techniques have the potential to make huge saving
in the bandwidth utilization. We propose spatial diversity techniques with and without
the assumption of perfect channel state information (CSI) at the transmitter.
We start with proposing improvement to signal-to-leakage ratio (SLR) maximization
based spatial multiplexing techniques for both fiat fading and frequency selective
channels. [Continues.
Over-the-Air Integrated Sensing, Communication, and Computation in IoT Networks
To facilitate the development of Internet of Things (IoT) services,
tremendous IoT devices are deployed in the wireless network to collect and pass
data to the server for further processing. Aiming at improving the data sensing
and delivering efficiency, the integrated sensing and communication (ISAC)
technique has been proposed to design dual-functional signals for both radar
sensing and data communication. To accelerate the data processing, the function
computation via signal transmission is enabled by over-the-air computation
(AirComp), which is based on the analog-wave addition property in a
multi-access channel. As a natural combination, the emerging technology namely
over-the-air integrated sensing, communication, and computation (Air-ISCC)
adopts both the promising performances of ISAC and AirComp to improve the
spectrum efficiency and reduce latency by enabling simultaneous sensing,
communication, and computation. In this article, we provide a promptly overview
of Air-ISCC by introducing the fundamentals, discussing the advanced
techniques, and identifying the applications
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