Likelihood inference for discretely observed Markov jump processes with finite state space is investigated. The existence and uniqueness of the maximum likelihood estimator of the intensity matrix are investigated. This topic is closely related to the imbedding problem for Markov chains. It is demonstrated that the maximum likelihood estimator can be found either by the EM algorithm or by a Mar...

The rejection sampling filter and smoother, proposed by Tanizaki (1996, 1999), Tanizaki and Mariano (1998) and Hürzeler and Künsch (1998), take a lot of time computationally. The Markov chain Monte Carlo smoother, developed by Carlin, Polson and Stoffer (1992), Carter and Kohn (1994, 1996) and Geweke and Tanizaki (1999a, 1999b), does not show a good performance depending on nonlinearity and non...

This paper presents a new system-wide harmonic state estimation method with the capability to identify harmonic sources with fewer meters than state variables. Note there are only a few simultaneous harmonic sources among the suspicious buses. By extending the concept of observability, the underdetermined system can be observable when considering the sparsity of harmonic sources. We formulate h...

Due to their inherent features, Mobile AdHoc Networks have proven their efficiency to exchange data between mobile nodes. The main issue in this type of a network is the delivery of data to a destination. Unfortunately, the mobility of nodes and the disturbances of the propagation channel lead to the increase of the loss rate, which undermines routing performances. To address this issue, routin...

In DUV photolithography, mask patterns and processes are increasing in complexity, while IC critical dimensions continue to shrink at a rapid pace. As a result, the proportional variability of the gate CD will increase to unacceptable levels unless a more sophisticated means of advanced process control is introduced. The proposed process control framework exploits scatterometry, which provides ...

This paper presents a simplified nonlinear observer for power systems by exploring the special features of the nonlinear power system model. Rather than directly applying the existing nonlinear observer theory to the 6 order nonlinear power system model, the model is first decoupled into a 3 order nonlinear subsystem and a 3 order linear subsystem. Low order observers are designed for each subs...

This paper presents an approach to vision-based target tracking with a neural network (NN) augmented Kalman filter as the adaptive target state estimator. The vision sensor onboard the follower (tracker) aircraft is a single camera. Real-time image processing implemented in the onboard flight computer is used to derive measurements of relative bearing (azimuth and elevation angles) and the maxi...

In this paper, we show a simple way to derive the p-shift finite impulse response (FIR) unbiased estimator (UE) recently proposed by Shmaliy for time-invariant discrete-time state-space models. We also examine its iterative Kalman-like form. We conclude that the Kalman-like algorithm can serve efficiently as an optimal estimator with large averaging horizons. It has better engineering features ...

We consider two nonparametric procedures for estimating a concave distribution function based on data corrupted with additive noise generated by a bounded decreasing density on (0,∞). For the maximum likelihood (ML) estimator and least squares (LS) estimator, we state qualitative properties, prove consistency and propose a computational algorithm. For the LS estimator and its derivative, we als...

This paper presents a method of improving the least square channel estimator accuracy without increasing the pilot density. The Kalman filter process equation can be represented as Autoregressive model, and the least square channel estimate is seen as a noisy measurement of the true channel state, so the Kalman filter measurement equation can be represented as the least square estimated channel...