51 research outputs found
Multiuser detection in a dynamic environment Part I: User identification and data detection
In random-access communication systems, the number of active users varies
with time, and has considerable bearing on receiver's performance. Thus,
techniques aimed at identifying not only the information transmitted, but also
that number, play a central role in those systems. An example of application of
these techniques can be found in multiuser detection (MUD). In typical MUD
analyses, receivers are based on the assumption that the number of active users
is constant and known at the receiver, and coincides with the maximum number of
users entitled to access the system. This assumption is often overly
pessimistic, since many users might be inactive at any given time, and
detection under the assumption of a number of users larger than the real one
may impair performance.
The main goal of this paper is to introduce a general approach to the problem
of identifying active users and estimating their parameters and data in a
random-access system where users are continuously entering and leaving the
system. The tool whose use we advocate is Random-Set Theory: applying this, we
derive optimum receivers in an environment where the set of transmitters
comprises an unknown number of elements. In addition, we can derive
Bayesian-filter equations which describe the evolution with time of the a
posteriori probability density of the unknown user parameters, and use this
density to derive optimum detectors. In this paper we restrict ourselves to
interferer identification and data detection, while in a companion paper we
shall examine the more complex problem of estimating users' parameters.Comment: To be published on IEEE Transactions on Information Theor
Radar networks: A review of features and challenges
Networks of multiple radars are typically used for improving the coverage and
tracking accuracy. Recently, such networks have facilitated deployment of
commercial radars for civilian applications such as healthcare, gesture
recognition, home security, and autonomous automobiles. They exploit advanced
signal processing techniques together with efficient data fusion methods in
order to yield high performance of event detection and tracking. This paper
reviews outstanding features of radar networks, their challenges, and their
state-of-the-art solutions from the perspective of signal processing. Each
discussed subject can be evolved as a hot research topic.Comment: To appear soon in Information Fusio
Multisensor Poisson Multi-Bernoulli Filter for Joint Target-Sensor State Tracking
In a typical multitarget tracking (MTT) scenario, the sensor state is either
assumed known, or tracking is performed in the sensor's (relative) coordinate
frame. This assumption does not hold when the sensor, e.g., an automotive
radar, is mounted on a vehicle, and the target state should be represented in a
global (absolute) coordinate frame. Then it is important to consider the
uncertain location of the vehicle on which the sensor is mounted for MTT. In
this paper, we present a multisensor low complexity Poisson multi-Bernoulli MTT
filter, which jointly tracks the uncertain vehicle state and target states.
Measurements collected by different sensors mounted on multiple vehicles with
varying location uncertainty are incorporated sequentially based on the arrival
of new sensor measurements. In doing so, targets observed from a sensor mounted
on a well-localized vehicle reduce the state uncertainty of other poorly
localized vehicles, provided that a common non-empty subset of targets is
observed. A low complexity filter is obtained by approximations of the joint
sensor-feature state density minimizing the Kullback-Leibler divergence (KLD).
Results from synthetic as well as experimental measurement data, collected in a
vehicle driving scenario, demonstrate the performance benefits of joint
vehicle-target state tracking.Comment: 13 pages, 7 figure
Asynchronous device detection for cognitive device-to-device communications
Dynamic spectrum sharing will facilitate the interference coordination in device-to-device (D2D) communications. In the absence of network level coordination, the timing synchronization among D2D users will be unavailable, leading to inaccurate channel state estimation and device detection, especially in time-varying fading environments. In this study, we design an asynchronous device detection/discovery framework for cognitive-D2D applications, which acquires timing drifts and dynamical fading channels when directly detecting the existence of a proximity D2D device (e.g. or primary user). To model and analyze this, a new dynamical system model is established, where the unknown timing deviation follows a random process, while the fading channel is governed by a discrete state Markov chain. To cope with the mixed estimation and detection (MED) problem, a novel sequential estimation scheme is proposed, using the conceptions of statistic Bayesian inference and random finite set. By tracking the unknown states (i.e. varying time deviations and fading gains) and suppressing the link uncertainty, the proposed scheme can effectively enhance the detection performance. The general framework, as a complimentary to a network-aided case with the coordinated signaling, provides the foundation for development of flexible D2D communications along with proximity-based spectrum sharing
The University Defence Research Collaboration In Signal Processing
This chapter describes the development of algorithms for automatic detection of anomalies from multi-dimensional, undersampled and incomplete datasets. The challenge in this work is to identify and classify behaviours as normal or abnormal, safe or threatening, from an irregular and often heterogeneous sensor network. Many defence and civilian applications can be modelled as complex networks of interconnected nodes with unknown or uncertain spatio-temporal relations. The behavior of such heterogeneous networks can exhibit dynamic properties, reflecting evolution in both network structure (new nodes appearing and existing nodes disappearing), as well as inter-node relations.
The UDRC work has addressed not only the detection of anomalies, but also the identification of their nature and their statistical characteristics. Normal patterns and changes in behavior have been incorporated to provide an acceptable balance between true positive rate, false positive rate, performance and computational cost. Data quality measures have been used to ensure the models of normality are not corrupted by unreliable and ambiguous data. The context for the activity of each node in complex networks offers an even more efficient anomaly detection mechanism. This has allowed the development of efficient approaches which not only detect anomalies but which also go on to classify their behaviour
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