Using sensors that only measure the bearing angle and range of an aircraft, a Kalman filter is implemented to track the range, range rate, bearing, and bearing rate of a maneuvering aircraft with unknown varying accelerations. Simulations will demonstrate the tracking performance of the Kalman filter with single and multiple prediction steps between the measurement step. Then, a Kalman filter w...

Fifty years ago Rudolph E. Kalman published his seminal work [1], for which he recently received the 2008 National Medal of Honor from President Obama. Today, Kalman fi lters are used widely in applications ranging from space systems to medical instruments to fi nance. Every year hundreds of papers are published concerning Kalman fi lters, and this text by Lewis, Xie, and Popa has managed to do...

The discrete Kalman lter, which is becoming a common tool for reducing uncertainty in robot navigation, suuers from some basic limitations when used for such applications. In this paper, we describe a recursive total least squares estimator (RTLS) as an alternative to the Kalman lter, and compare their performances in three sets of experiments involving problems in robot navigation. In all case...

In this paper, we investigate the implementation of a Python code for a Kalman Filter using the Numpy package. A Kalman Filtering is carried out in two steps: Prediction and Update. Each step is investigated and coded as a function with matrix input and output. These different functions are explained and an example of a Kalman Filter application for the localization of mobile in wireless networ...

We propose a hybrid forecast based on extended discrete grey Markov and variable dimension Kalman model and show that our hybrid model can improve much more the performance of forecast than traditional grey Markov and Kalman models. Our simulation results are given to demonstrate that our hybrid forecast method combined with degree of grey incidence are better than grey Markov and ARFIMA model ...

nowadays with increasing use of numerous nonlinear loads, voltage and current harmonics in power systems are one of the most important problems power engineers encounter. many of these nonlinear loads, because of their dynamic natures, inject time-varying harmonics into power system. common techniques applied for harmonics measurement and assessment such as fft have significant errors in presen...

futures contract is one of the most important derivatives that is used in financial markets in all over the world to buy or sell an asset or commodity in the future. pricing of this tool depends on expected price of asset or commodity at the maturity date. according to this, theoretical futures pricing models try to find this expected price in order to use in the futures contract. so in this ar...

For linear dynamic systems with white process and measurement noise, the Kalman filter is known to be an optimal estimator. In the application of Kalman filters there is often known model or signal information that is either ignored or dealt with heuristically [1]. This work presents a way to generalize the Kalman filter in such a way that known relations among the state variables (i.e., state ...

The Kalman Filter is a statistical method that involves an algorithm which provides an efficient recursive approach to estimatating the states of a process by minimizing the mean of the squared error. The filter is a powerful tool in statistical signal processing that allows for acurate estimations of past, present and future states, even with an incomplete or imprecise system model.

This paper presents the maiden application of a variant of Kalman Filter algorithm known as Local Ensemble Transform based Kalman Filter (LET-KF) for power system harmonic estimation. The proposed algorithm is applied for estimating the harmonic parameters of a power signal containing harmonics, sub-harmonics, inter-harmonics in presence of white Gaussian noise. These algorithms are applied and...