155 research outputs found
Optimal Precoders for Tracking the AoD and AoA of a mm-Wave Path
In millimeter-wave channels, most of the received energy is carried by a few
paths. Traditional precoders sweep the angle-of-departure (AoD) and
angle-of-arrival (AoA) space with directional precoders to identify directions
with largest power. Such precoders are heuristic and lead to sub-optimal
AoD/AoA estimation. We derive optimal precoders, minimizing the Cram\'{e}r-Rao
bound (CRB) of the AoD/AoA, assuming a fully digital architecture at the
transmitter and spatial filtering of a single path. The precoders are found by
solving a suitable convex optimization problem. We demonstrate that the
accuracy can be improved by at least a factor of two over traditional
precoders, and show that there is an optimal number of distinct precoders
beyond which the CRB does not improve.Comment: Resubmission to IEEE Trans. on Signal Processing. 12 pages and 9
figure
Analysis of A Nonsmooth Optimization Approach to Robust Estimation
In this paper, we consider the problem of identifying a linear map from
measurements which are subject to intermittent and arbitarily large errors.
This is a fundamental problem in many estimation-related applications such as
fault detection, state estimation in lossy networks, hybrid system
identification, robust estimation, etc. The problem is hard because it exhibits
some intrinsic combinatorial features. Therefore, obtaining an effective
solution necessitates relaxations that are both solvable at a reasonable cost
and effective in the sense that they can return the true parameter vector. The
current paper discusses a nonsmooth convex optimization approach and provides a
new analysis of its behavior. In particular, it is shown that under appropriate
conditions on the data, an exact estimate can be recovered from data corrupted
by a large (even infinite) number of gross errors.Comment: 17 pages, 9 figure
Fitting Jump Models
We describe a new framework for fitting jump models to a sequence of data.
The key idea is to alternate between minimizing a loss function to fit multiple
model parameters, and minimizing a discrete loss function to determine which
set of model parameters is active at each data point. The framework is quite
general and encompasses popular classes of models, such as hidden Markov models
and piecewise affine models. The shape of the chosen loss functions to minimize
determine the shape of the resulting jump model.Comment: Accepted for publication in Automatic
Physiological Control of Human Heart Rate and Oxygen Consumption during Rhythmic Exercises
Physical exercise has significant benefits for humans in improving the health
and quality of their lives, by improving the functional performance of their
cardiovascular and respiratory systems. However, it is very important to
control the workload, e.g. the frequency of body movements, within the
capability of the individual to maximise the efficiency of the exercise. The
workload is generally represented in terms of heart rate (HR) and oxygen
consumption VO2. We focus particularly on the control of HR and VO2 using the
workload of an individual body movement, also known as the exercise rate (ER),
in this research. The first part of this report deals with the modelling and
control of HR during an unknown type of rhythmic exercise. A novel feature of
the developed system is to control HR via manipulating ER as a control input.
The relation between ER and HR is modelled using a simple autoregressive model
with unknown parameters. The parameters of the model are estimated using a
Kalman filter and an indirect adaptive H1 controller is designed. The
performance of the system is tested and validated on six subjects during rowing
and cycling exercise. The results demonstrate that the designed control system
can regulate HR to a predefined profile. The second part of this report deals
with the problem of estimating VO2 during rhythmic exercise, as the direct
measurement of VO2 is not realisable in these environments. Therefore,
non-invasive sensors are used to measure HR, RespR, and ER to estimate VO2. The
developed approach for cycling and rowing exercise predicts the percentage
change in maximum VO2 from the resting to the exercising phases, using a
Hammerstein model.. Results show that the average quality of fit in both
exercises is improved as the intensity of exercise is increased
A New Path Planning Guidance Law For Improved Impact Time Control of Missiles and Precision Munitions
A new missile guidance law is proposed for the control of impact time which provides an improved time-to-go calculation by removing error due to trajectory curvature and also provides a family of trajectories for trajectory planning purposes. Unlike conventional optimal guidance laws, the proposed law is non explicit in time-to-go and the linearization of the engagement kinematics in order to gain a closed form solution is not necessary
Regularized System Identification
This open access book provides a comprehensive treatment of recent developments in kernel-based identification that are of interest to anyone engaged in learning dynamic systems from data. The reader is led step by step into understanding of a novel paradigm that leverages the power of machine learning without losing sight of the system-theoretical principles of black-box identification. The authors’ reformulation of the identification problem in the light of regularization theory not only offers new insight on classical questions, but paves the way to new and powerful algorithms for a variety of linear and nonlinear problems. Regression methods such as regularization networks and support vector machines are the basis of techniques that extend the function-estimation problem to the estimation of dynamic models. Many examples, also from real-world applications, illustrate the comparative advantages of the new nonparametric approach with respect to classic parametric prediction error methods. The challenges it addresses lie at the intersection of several disciplines so Regularized System Identification will be of interest to a variety of researchers and practitioners in the areas of control systems, machine learning, statistics, and data science. This is an open access book
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