56,321 research outputs found
System identification, time series analysis and forecasting:The Captain Toolbox handbook.
CAPTAIN is a MATLAB compatible toolbox for non stationary time series analysis, system identification, signal processing and forecasting, using unobserved components models, time variable parameter models, state dependent parameter models and multiple input transfer function models. CAPTAIN also includes functions for true digital control
Dual polarization nonlinear Fourier transform-based optical communication system
New services and applications are causing an exponential increase in internet
traffic. In a few years, current fiber optic communication system
infrastructure will not be able to meet this demand because fiber nonlinearity
dramatically limits the information transmission rate. Eigenvalue communication
could potentially overcome these limitations. It relies on a mathematical
technique called "nonlinear Fourier transform (NFT)" to exploit the "hidden"
linearity of the nonlinear Schr\"odinger equation as the master model for
signal propagation in an optical fiber. We present here the theoretical tools
describing the NFT for the Manakov system and report on experimental
transmission results for dual polarization in fiber optic eigenvalue
communications. A transmission of up to 373.5 km with bit error rate less than
the hard-decision forward error correction threshold has been achieved. Our
results demonstrate that dual-polarization NFT can work in practice and enable
an increased spectral efficiency in NFT-based communication systems, which are
currently based on single polarization channels
Delayed Dynamical Systems: Networks, Chimeras and Reservoir Computing
We present a systematic approach to reveal the correspondence between time
delay dynamics and networks of coupled oscillators. After early demonstrations
of the usefulness of spatio-temporal representations of time-delay system
dynamics, extensive research on optoelectronic feedback loops has revealed
their immense potential for realizing complex system dynamics such as chimeras
in rings of coupled oscillators and applications to reservoir computing.
Delayed dynamical systems have been enriched in recent years through the
application of digital signal processing techniques. Very recently, we have
showed that one can significantly extend the capabilities and implement
networks with arbitrary topologies through the use of field programmable gate
arrays (FPGAs). This architecture allows the design of appropriate filters and
multiple time delays which greatly extend the possibilities for exploring
synchronization patterns in arbitrary topological networks. This has enabled us
to explore complex dynamics on networks with nodes that can be perfectly
identical, introduce parameter heterogeneities and multiple time delays, as
well as change network topologies to control the formation and evolution of
patterns of synchrony
Optoelectronic Reservoir Computing
Reservoir computing is a recently introduced, highly efficient bio-inspired
approach for processing time dependent data. The basic scheme of reservoir
computing consists of a non linear recurrent dynamical system coupled to a
single input layer and a single output layer. Within these constraints many
implementations are possible. Here we report an opto-electronic implementation
of reservoir computing based on a recently proposed architecture consisting of
a single non linear node and a delay line. Our implementation is sufficiently
fast for real time information processing. We illustrate its performance on
tasks of practical importance such as nonlinear channel equalization and speech
recognition, and obtain results comparable to state of the art digital
implementations.Comment: Contains main paper and two Supplementary Material
Fast recursive filters for simulating nonlinear dynamic systems
A fast and accurate computational scheme for simulating nonlinear dynamic
systems is presented. The scheme assumes that the system can be represented by
a combination of components of only two different types: first-order low-pass
filters and static nonlinearities. The parameters of these filters and
nonlinearities may depend on system variables, and the topology of the system
may be complex, including feedback. Several examples taken from neuroscience
are given: phototransduction, photopigment bleaching, and spike generation
according to the Hodgkin-Huxley equations. The scheme uses two slightly
different forms of autoregressive filters, with an implicit delay of zero for
feedforward control and an implicit delay of half a sample distance for
feedback control. On a fairly complex model of the macaque retinal horizontal
cell it computes, for a given level of accuracy, 1-2 orders of magnitude faster
than 4th-order Runge-Kutta. The computational scheme has minimal memory
requirements, and is also suited for computation on a stream processor, such as
a GPU (Graphical Processing Unit).Comment: 20 pages, 8 figures, 1 table. A comparison with 4th-order Runge-Kutta
integration shows that the new algorithm is 1-2 orders of magnitude faster.
The paper is in press now at Neural Computatio
An aircraft sensor fault tolerant system
The design of a sensor fault tolerant system which uses analytical redundancy for the Terminal Configured Vehicle (TCV) research aircraft in a Microwave Landing System (MLS) environment was studied. The fault tolerant system provides reliable estimates for aircraft position, velocity, and attitude in the presence of possible failures in navigation aid instruments and onboard sensors. The estimates, provided by the fault tolerant system, are used by the automated guidance and control system to land the aircraft along a prescribed path. Sensor failures are identified by utilizing the analytic relationship between the various sensor outputs arising from the aircraft equations of motion
GPS Carrier Tracking Loop Performance in the presence of Ionospheric Scintillations
The performance of several GPS carrier tracking loops
is evaluated using wideband GPS data recorded during
strong ionospheric scintillations. The aim of this study is
to determine the loop structures and parameters that enable
good phase tracking during the power fades and phase
dynamics induced by scintillations. Constant-bandwidth
and variable-bandwidth loops are studied using theoretical
models, simulation, and tests with actual GPS signals.
Constant-bandwidth loops with loop bandwidths near 15
Hz are shown to lose phase lock during scintillations. Use
of the decision-directed discriminator reduces the carrier
lock threshold by ∼1 dB relative to the arctangent and conventional Costas discriminators. A proposed variablebandwidth
loop based on a Kalman filter reduces the carrier
lock threshold by more than 7 dB compared to a 15-Hz
constant-bandwidth loop. The Kalman filter-based strategy
employs a soft-decision discriminator, explicitly models
the effects of receiver clock noise, and optimally adapts
the loop bandwidth to the carrier-to-noise ratio. In extensive
simulation and in tests using actual wideband GPS
data, the Kalman filter PLL demonstrates improved cycle
slip immunity relative to constant bandwidth PLLs.Aerospace Engineering and Engineering Mechanic
Effective denoising and adaptive equalization of indoor optical wireless channel with artificial light using the discrete wavelet transform and artificial neural network
Indoor diffuse optical wireless (OW) communication systems performance is limited due to a number of effects; interference from natural and artificial light sources and multipath induced intersymbol interference (ISI). Artificial light interference (ALI) is a periodic signal with a spectrum profile extending up to the MHz range. It is the dominant source of performance degradation at low data rates, which can be removed using a high-pass filter (HPF). On the other hand, ISI is more severe at high data rates and an equalizing filter is incorporated at the receiver to compensate for the ISI. This paper provides the simulation results for a discrete wavelet transform (DWT)—artificial neural network (ANN)-based receiver architecture for on-and-off keying (OOK) non-return-to-zero (NRZ) scheme for a diffuse indoor OW link in the presence of ALI and ISI. ANN is adopted for classification acting as an efficient equalizer compared to the traditional equalizers. The ALI is effectively reduced by proper selection of the DWT coefficients resulting in improved receiver performance compared to the digital HPF. The simulated bit error rate (BER) performance of proposed DWT-ANN receiver structure for a diffuse indoor OW link operating at a data range of 10-200 Mbps is presented and discussed. The results are compared with performance of a diffuse link with an HPF-equalizer, ALI with/without filtering, and a line-of-sight (LOS) without filtering. We show that the DWT-ANN display a lower power requirement when compared to the receiver with an HPF-equalizer over a full range of delay spread in presence of ALI. However, as expected compared to the ideal LOS link the power penalty is higher reaching to 6 dB at 200 Mbps data rate
Dynamic Models and Nonlinear Filtering of Wave Propagation in Random Fields
In this paper, a general model of wireless channels is established based on
the physics of wave propagation. Then the problems of inverse scattering and
channel prediction are formulated as nonlinear filtering problems. The
solutions to the nonlinear filtering problems are given in the form of dynamic
evolution equations of the estimated quantities. Finally, examples are provided
to illustrate the practical applications of the proposed theory.Comment: 12 pages, 1 figur
Geometrically Intrinsic Nonlinear Recursive Filters I: Algorithms
The Geometrically Intrinsic Nonlinear Recursive Filter, or GI Filter, is
designed to estimate an arbitrary continuous-time Markov diffusion process X
subject to nonlinear discrete-time observations. The GI Filter is fundamentally
different from the much-used Extended Kalman Filter (EKF), and its second-order
variants, even in the simplest nonlinear case, in that: (i) It uses a quadratic
function of a vector observation to update the state, instead of the linear
function used by the EKF. (ii) It is based on deeper geometric principles,
which make the GI Filter coordinate-invariant. This implies, for example, that
if a linear system were subjected to a nonlinear transformation f of the
state-space and analyzed using the GI Filter, the resulting state estimates and
conditional variances would be the push-forward under f of the Kalman Filter
estimates for the untransformed system - a property which is not shared by the
EKF or its second-order variants.
The noise covariance of X and the observation covariance themselves induce
geometries on state space and observation space, respectively, and associated
canonical connections. A sequel to this paper develops stochastic differential
geometry results - based on "intrinsic location parameters", a notion derived
from the heat flow of harmonic mappings - from which we derive the
coordinate-free filter update formula. The present article presents the
algorithm with reference to a specific example - the problem of tracking and
intercepting a target, using sensors based on a moving missile. Computational
experiments show that, when the observation function is highly nonlinear, there
exist choices of the noise parameters at which the GI Filter significantly
outperforms the EKF.Comment: 22 pages, 4 figure
- …