Real-time Estimation Algorithm Research Articles

Reading a Neural Code

Traditional approaches to neural coding characterize the encoding of known stimuli in average neural responses. Organisms face nearly the opposite task--extracting information about an unknown time-dependent stimulus from short segments of a spike train. Here the neural code was characterized from the point of view of the organism, culminating in algorithms for real-time stimulus estimation based on a single example of the spike train. These methods were applied to an identified movement-sensitive neuron in the fly visual system. Such decoding experiments determined the effective noise level and fault tolerance of neural computation, and the structure of the decoding algorithms suggested a simple model for real-time analog signal processing with spiking neurons.

Nonlinear Dynamic Model and Parameter Estimation for Fluidized Catalytic Cracking Unit

This paper presents a development of nonlinear system decomposed state space model for a large scale fluidized catalytic cracking unit (FCCU) and the application of parameter estimation techniques to estimating the model parameters. In order to make the model as developed more robust, the real time estimation algorithm has also been proposed for the problem under investigationThe resulting model has been verified through the industrial test data gathered from a real production scale FCCU

Recursive image registration with application to motion estimation

An algorithm for real-time recursive estimation of the two-dimensional shift between successive images in an image sequence occurring, e.g., in a down-looking airborne TV sensor, is presented. The shift, which is linearly related to ground velocity, is adaptively tracked by means of the gradient of a similarity function relating the two successive images. An implementation is presented in which substantial memory capacity and computational complexity are saved by using only a single line in the image frame and by binary quantization of the video signal. A detailed analysis of the algorithm is presented using stochastic mathematical models for the terrain texture, image noise, and velocity variations. Under some simplifying assumptions, closed-form solutions for the error statistics, including temporal power spectral density, are derived. The probability of loss of lock in tracking and the expected time for its reacquisition are evaluated and are found to be low at typical operating conditions. The analysis also indicates considerable robustness of the algorithm to widely different image statistics. Numerical examples indicate very good performance in autonomous navigation applications.

Sequential Piecewise Recursive Filter for GPS Low-Dynamics Navigation

The design, implementation, and performance of a real-time estimation algorithm, referred to in this paper as the sequential piecewise recursive (SPWR) algorithm, for the global-positioning system (GPS) low-dynamics navigation system is described. The SPWR algorithm for this application was implemented in single precision arithmetic (32 bit, floating point). Numerical results are presented covariance and filter gains at a slower rate than the state measurement update, and it uses U-D factor formulation to perform covariance computations. The SPWR algorithm saves real-time processing requirements without appreciable degradation of filter performance. Another important feature of the SPWR algorithm is that it incorporates pseudorange and delta-range data from each GPS satellite sequentially for navigation solution. The SPWR algorithm, for this application, was implemented in single precision arithmetic (32 bit, floating point). Numerical results are presented.

A New Algorithm for Real-Time Estimation of the State of a Power System

The problem of real-time estimation of the state of a power system is treated in a practical sense, focusing our attention on the reduction of the computing time of the estimation algorithm and the reliability of the computed state. Since under normal operating conditions the power system behaves in a quasistatic manner, the state estimation can be updated at any sampling period. In such a case the use of a quadratic performance index and the particularity of the measurement functions enable the design of a simple tracking state estimation algorithm. At the same time, a meter failure can be detected and the corresponding measurement deleted.

Algorithms for Real Time Estimation of the State of Large Electroenergetical Systems

The estimation of the state of power system networks represents a very important industrial task. The greatest problem to overcome in the estimation is the big dimensionality of the estate vector altogether with the nonlinear relation between states and measurements. Several methode for the state estimation were tested. The analysis and simulations have shown that the square root method should be prefered, namely the algorithm REFILV.