State estimation theory is one of the best mathematical approaches to analyze variants in the states of the system or process. The state of the system is defined by a set of variables that provide a complete representation of the internal condition at any given instant of time. Filtering of Random processes is referred to as Estimation, and is a well-defined statistical technique. There are two...

It has been shown that the narrow-band (NB) interference suppression capability of a direct-sequence @S) spread spectrum system can be enhanced considerably by processing the received signal via a prediction error filter. The conventional approach to this problem makes use of a linear filter. However, the binary DS signal, that acts as noise in the prediction process, is highly non-Gaussian. Th...

A novel generalized play operator-based (GPO-based) nonlinear adaptive filter is proposed to model rate-dependent hysteresis nonlinearity for smart actuators. In the proposed filter, the input signal vector consists of the output of a tapped delay line. GPOs with various thresholds are used to construct a nonlinear network and connected with the input signals. The output signal of the filter is...

This paper presents a generalization of the Kalman filter for linear and nonlinear fractional order discrete state-space systems. Linear and nonlinear discrete fractional order state-space systems are also introduced. The simplified kalman filter for the linear case is called the fractional Kalman filter and its nonlinear extension is named the extended fractional Kalman filter. The background ...

the radar tracking is one of the best leo satellite tracking methods. while the tracking filters which are mostly linear, and them are not able to have a precise estimation of the objects with nonlinear motion dynamic such as satellite, we should use nonlinear filters. in this paper , firstly, we deal with the problem of the leo satellites motion path modeling according to the satellite motion ...

The nonlinear filters based on Taylor series approximation are broadly used for computational simplicity, even though their filtering estimates are clearly biased. In this paper, first, we analyze what is approximated when we apply the expanded nonlinear functions to the standard linear recursive Kalman filter algorithm. Next, since the state variables αt and αt−1 are approximated as a conditio...

this paper presents an innovative active power filter design method to simultaneously compensate the current harmonics and reactive power of a nonlinear load. the power filter integrates a passive power filter which is a rl low-pass filter placed in series with the load, and an active power filter which comprises an rl in series with an igbt based voltage source converter. the filter is assumed...

This paper presents a generalization of the Kalman Filter for linear and nonlinear fractional order discrete state-space systems. The linear and nonlinear discrete fractional order state-space systems are also introduced. The simplified Kalman Filter for linear case is called Fractional Kalman Filter and its nonlinear extension is named Extended Fractional Kalman Filter respectively. The backgr...

Matched filters are the optimal linear filters for signal detection under linear channel and white noise conditions. Their optimality is guaranteed in the additive white Gaussian noise channel due to the sufficiency of second order statistics. In this paper, we introduce a nonlinear filter for signal detection based on the Cauchy-Schwartz quadratic mutual information (CS-QMI) criterion. This fi...

Extended depth of field (EDF) microscopy, achieved through computational optics, allows for real-time 3D imaging of live cell dynamics. EDF is achieved through a combination of point spread function engineering and digital image processing. A linear Wiener filter has been conventionally used to deconvolve the image, but it suffers from high frequency noise amplification and processing artifacts...