State estimation is the common problem in every area of engineering. There are different filters used to overcome the problem of state estimation like Kalman filter, Particle filters etc. Kalman Filter is popular when the system is linear but when the system is highly non-linear then the different derivatives of Kalman Filter are used like Extended Kalman Filter (EKF), Unscented Kalman filter. ...

This paper proposes a new linear finite impulse response (FIR) filter called the best linear unbiased FIR (BLUF) filter for the state estimation in continuous-time state space models. The proposed BLUF filter for continuous-time state space models is obtained by a formal limiting procedure of discretized systems. The BLUF filter is represented in an iterative form and then in a standard FIR for...

Minimizing forecast error requires accurately specifying the initial state from which the forecast is made by optimally using available observing resources to obtain the most accurate possible analysis. The Kalman filter accomplishes this for linear systems and experience shows that the extended Kalman filter also performs well in nonlinear systems. Unfortunately, the Kalman filter and the exte...

This paper deals with the problem of maneuvering target tracking in wireless tracking service. It results in a mixed linear/non-linear Models estimation problem. For maneuvering tracking systems, these problems are traditionally handled using the extended Kalman filter or Particle filter. In this paper, Marginalized Particle Filter is presented for applications in such problem. The algorithm ma...

There is no single standard technique or methodology to characterize the size, structure, number, and chemical composition of airborne carbon nanotubes.  Existing analytical instruments and analytical techniques for evaluating nanoparticle concentrations cannot simultaneously provide morphology, state of agglomeration, surface area, mass, size distribution and chemical composition data critical...

This article studies the distributed state estimation in sensor network, where $m$ sensors are deployed to infer notation="LaTeX">$n$ -dimensional of a linear time-invariant Gaussian system. By lossless decom...

In this paper we analyze the convergence of a family of robust Kalman filters. For each filter of this family the model uncertainty is tuned according to the so called tolerance parameter. Assuming that the corresponding state-space model is reachable and observable, we show that the corresponding Riccati-like mapping is strictly contractive provided that the tolerance is sufficiently small, ac...

This paper proves the invariance of the second-order modes of continuous-time systems under general frequency transformation, that is, under reactance function. The invariance is proved by obtaining the state-space representation of the transformed system, and its controllability / observability grammians in terms of those of the original system and the reactance system. This invariance propert...

Due to widespread use of Global Positioning System (GPS) in different applications, the issue of GPS signal interference cancelation is becoming an increasing concern. One of the most important intentional interferences is spoofing signals. An effective interference (delay spoof) reduction method based on adaptive filtering is developed in this paper. The principle of method is using adaptive f...