Optimal or unbiased estimators are widely used for state estimation and tracking. We propose a new minimum variance unbiased (MVU) finite impulse response (FIR) filter which minimizes the estimation error variance in the unbiased FIR (UFIR) filter. The relationship between the filter gains of the MVU FIR, UFIR and optimal FIR (OFIR) filters is found analytically. Simulations provided using a po...

For fault-tolerant real-time filtering, an efficient two-filter architecture is proposed. In the two-filter architecture, a host filter is operated permanently. A compression filter is intermittently initialized and operated during prescribed time intervals. to handle measurements susceptible to soft faults. New compression filters are derived based on time-propagated measurement concept. The d...

This paper analyses the velocity estimation of a target, from the Doppler filter using 1) Kalman filter 2) Adaptive Kalman filter 3) Kalman filter with state vector fusion 4) Adaptive Kalman filter with state vector fusion 5) State vector fused adaptive Kalman filter. Simulation through MATLAB gave good response for 4 and 5 algorithms under low signal to noise ratio. 2 and 3 algorithms gave bet...

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...