547 research outputs found
Optimal Transmit Beamforming for Integrated Sensing and Communication
This paper studies the transmit beamforming in a downlink integrated sensing
and communication (ISAC) system, where a base station (BS) equipped with a
uniform linear array (ULA) sends combined information-bearing and dedicated
radar signals to simultaneously perform downlink multiuser communication and
radar target sensing. Under this setup, we maximize the radar sensing
performance (in terms of minimizing the beampattern matching errors or
maximizing the minimum weighted beampattern gains), subject to the
communication users' minimum signal-to-interference-plus-noise ratio (SINR)
requirements and the BS's transmit power constraints. In particular, we
consider two types of communication receivers, namely Type-I and Type-II
receivers, which do not have and do have the capability of cancelling the
interference from the {\emph{a-priori}} known dedicated radar signals,
respectively. Under both Type-I and Type-II receivers, the beampattern matching
and minimum weighted beampattern gain maximization problems are globally
optimally solved via applying the semidefinite relaxation (SDR) technique
together with the rigorous proof of the tightness of SDR for both Type-I and
Type-II receivers under the two design criteria. It is shown that at the
optimality, radar signals are not required with Type-I receivers under some
specific conditions, while radar signals are always needed to enhance the
performance with Type-II receivers. Numerical results show that the minimum
weighted beampattern gain maximization leads to significantly higher
beampattern gains at the worst-case sensing angles with a much lower
computational complexity than the beampattern matching design. We show that by
exploiting the capability of canceling the interference caused by the radar
signals, the case with Type-II receivers results in better sensing performance
than that with Type-I receivers and other conventional designs.Comment: submitted for possible journal publicatio
Feature detection algorithms in computed images
The problem of sensing a medium by several sensors and retrieving
interesting features is a very general one. The basic framework of the
problem is generally the same for applications from MRI,
tomography, Radar SAR imaging to subsurface imaging, even though the
data acquisition processes, sensing geometries and sensed properties are
different. In this thesis we introduced a new perspective to the
problem of remote sensing and information retrieval by studying the
problem of subsurface imaging using GPR and seismic sensors.
We have shown that if the sensed medium is sparse in some domain then it can be imaged using many fewer measurements than required by the standard methods. This leads to much lower data acquisition times and better images representing the medium. We have used the ideas from Compressive Sensing, which show that a small number of random measurements about a signal is sufficient to completely characterize it, if the signal is sparse or compressible in some domain. Although we have applied our ideas to the subsurface imaging problem, our results are general and can be extended to other remote sensing applications.
A second objective in remote sensing is information retrieval
which involves searching for important features in the computed image of
the medium. In this thesis we focus on detecting buried structures like
pipes, and tunnels in computed GPR or seismic images. The problem of
finding these structures in high clutter and noise conditions, and
finding them faster than the standard shape detecting methods like the
Hough transform is analyzed.
One of the most important contributions of this thesis is, where the
sensing and the information retrieval stages are unified in a single
framework using compressive sensing. Instead of taking lots of standard
measurements to compute the image of the medium and search the
necessary information in the computed image, a much smaller number of
measurements as random projections are taken. The
data acquisition and information retrieval stages are unified by using a
data model dictionary that connects the information to the sensor data.Ph.D.Committee Chair: McClellan, James H.; Committee Member: Romberg, Justin K.; Committee Member: Scott, Waymond R. Jr.; Committee Member: Vela, Patricio A.; Committee Member: Vidakovic, Bran
Exploiting Sparse Structures in Source Localization and Tracking
This thesis deals with the modeling of structured signals under different sparsity constraints. Many phenomena exhibit an inherent structure that may be exploited when setting up models, examples include audio waves, radar, sonar, and image objects. These structures allow us to model, identify, and classify the processes, enabling parameter estimation for, e.g., identification, localisation, and tracking.In this work, such structures are exploited, with the goal to achieve efficient localisation and tracking of a structured source signal. Specifically, two scenarios are considered. In papers A and B, the aim is to find a sparse subset of a structured signal such that the signal parameters and source locations maybe estimated in an optimal way. For the sparse subset selection, a combinatorial optimization problem is approximately solved by means of convex relaxation, with the results of allowing for different types of a priori information to be incorporated in the optimization. In paper C, a sparse subset of data is provided, and a generative model is used to find the location of an unknown number of jammers in a wireless network, with the jammers’ movement in the network being tracked as additional observations become available
Multiple-Target Tracking in Complex Scenarios
In this dissertation, we develop computationally efficient algorithms for multiple-target tracking: MTT) in complex scenarios. For each of these scenarios, we develop measurement and state-space models, and then exploit the structure in these models to propose efficient tracking algorithms. In addition, we address design issues such as sensor selection and resource allocation.
First, we consider MTT when the targets themselves are moving in a
time-varying multipath environment. We develop a sparse-measurement model that allows us to exploit the inherent joint delay-Doppler diversity offered by the environment. We then reformulate the problem of MTT as a
block-support recovery problem using the sparse measurement model. We exploit the structure of the dictionary matrix to develop a computationally efficient block support recovery algorithm: and thereby a
multiple-target tracking algorithm) under the assumption that the channel state describing the time-varying multipath environment is known. Further, we also derive an upper bound on the
overall error probability of wrongly identifying the support of the sparse signal. We then relax the assumption that the channel state is known. We develop a new particle filter called
the Multiple Rao-Blackwellized Particle Filter: MRBPF) to jointly estimate
both the target and the channel states. We also compute the posterior Cramér-Rao bound: PCRB) on the estimates
of the target and the channel states and use the PCRB to find a
suitable subset of antennas to be used for transmission in each tracking interval,
as well as the power transmitted by these antennas.
Second, we consider the problem of tracking an unknown number and types of targets using a multi-modal sensor network. In a multi-modal sensor network, different quantities associated with the same state are measured using sensors of different kinds. Hence, an efficient method that can suitably combine the diverse information measured by each sensor is required. We first develop a Hierarchical Particle Filter: HPF) to estimate the unknown state from the multi-modal measurements for a special class of problems which can be modeled hierarchically. We then model our problem of
tracking using a hierarchical model and then use the proposed HPF for joint initiation, termination and tracking of multiple targets. The multi-modal data consists of the measurements collected from a radar, an
infrared camera and a human scout. We also propose a unified framework for multi-modal sensor management
that comprises sensor selection: SS), resource allocation: RA) and data fusion: DF). Our approach is inspired by the trading behavior of economic agents in commercial markets. We model the sensors and the sensor manager as economic agents, and the interaction among them as a double sided market with both consumers and producers. We propose an iterative double auction mechanism for computing the equilibrium of such a market. We relate the equilibrium point to the solutions of SS, RA and DF.
Third, we address MTT problem in the presence of data association
ambiguity that arises due to clutter. Data association corresponds to the problem
of assigning a measurement to each target. We treat the data association
and state estimation as separate subproblems. We develop a game-theoretic
framework to solve the data association, in which we model each tracker as
a player and the set of measurements as strategies. We develop utility functions
for each player, and then use a regret-based learning algorithm to find the
correlated equilibrium of this game. The game-theoretic approach allows us to associate
measurements to all the targets simultaneously. We then use particle filtering
on the reduced dimensional state of each target, independently
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