This report examined several methods for estimating Origin-Destination (OD) matrices for freeways, using loop detector data. Least squares based methods were compared in terms of both off-line and on-line estimation. Simulated data and observed data were used for evaluating the static and recursive estimators. For off-line estimation, four fully constrained least squares methods were compared. ...

The application of state-space-based subspace system identification methods to training-based estimation for quasi-static multiinput–multi-output (MIMO) frequency-selective channels is explored with the motivation for better model approximation performance. A modification of the traditional subspace methods is derived to suit the non-contiguous nature of training data in mobile communication sy...

Recursive time series methods are very popular due to their numerical simplicity. Their theoretical background is usually based on Kalman filtering in state space models (mostly in dynamic linear systems). However, in time series practice one must face frequently to outlying values (outliers), which require applying special methods of robust statistics. In the paper a simple robustification of ...

Low and time-changing inertia values due to the high percentage of renewable energy sources (RESs) can cause stability problems in power systems rapid frequency instabilities. Inertia monitoring will assist operators apply suitable actions more proper control methods alleviate issues. Therefore, this paper proposes an online method estimate total a network using recursive least-squares approach...

Abstract: This paper focuses on the recursive identification problems for a multivariate output-error system. By decomposing the system into several subsystems and by forming a coupled relationship between the parameter estimation vectors of the subsystems, two coupled auxiliary model based recursive least squares (RLS) algorithms are presented. Moreover, in contrast to the auxiliary model base...

The Kalman filter is the workhorse of target tracking. As is well known, it is the recursive linear minimum mean-square error (LMMSE) filter for a linear system under its stated assumptions. It is little known, however, that for many linear systems the LMMSE filter does not have a recursive form—in other words, it is not recursible. In this paper, we introduce the concept of recursibility, that...

We present a comparative study for tracking multiple persons using cameras with overlapping views. The evaluated methods consist of two batch mode trackers (Berclaz et al, 2011, Ben-Shitrit et al, 2011) and one recursive tracker (Liem and Gavrila, 2011), which integrate appearance cues and temporal information differently. We also added our own improved version of the recursive tracker. Further...

The random sample consensus (RANSAC) algorithm is frequently used in computer vision to estimate the parameters of a signal in the presence of noisy and even spurious observations called gross errors. Instead of just one signal, we desire to estimate the parameters of multiple signals, where at each time step a set of observations of generated from the underlying signals and gross errors are re...

All climate models agree that the temperature in Greece will increase in the range of 1° to 2°C by the year 2030 and mean sea level in Mediterranean is expected to rise at the rate of 5 cm/decade. The aim of the present paper is the estimation of the coastline displacement driven by the climate change and sea level rise. In order to achieve that, all known statistical and non-statistical comput...

In a Markov decision problem with hidden state variables, a posterior distribution serves as a state variable and Bayes’ law under an approximating model gives its law of motion. A decision maker expresses fear that his model is misspecified by surrounding it with a set of alternatives that are nearby when measured by their expected log likelihood ratios (entropies). Martingales represent alter...