We study the problem of designing spoofing signals to corrupt and mislead the output of a Kalman filter. Unlike existing works that focus on detection and filtering algorithms for the observer, we study the problem from the attacker’s point-of-view. In our model, the attacker can corrupt the measurements by adding spoofing signals. The attacker seeks to create a separation between the estimate ...

Compared to normal learning algorithms, for example backpropagation, the optimal bounded ellipsoid (OBE) algorithm has some better properties, such as faster convergence, since it has a similar structure as Kalman filter. OBE has some advantages over Kalman filter training, the noise is not required to be Guassian. In this paper OBE algorithm is applied traing the weights of recurrent neural ne...

A nonlinear semi-analytic filtering method to sequentially estimate spacecraft states and their associated uncertainties is presented. We first discuss the state transition tensors that characterize the localized nonlinear behavior of the spacecraft trajectory and illustrate the importance of higher order effects on orbit uncertainty propagation. We then present the semi-analytic filtering meth...

We consider the problem of optimal state estimation for a wide class of nonlinear time series models. A modified sigma point filter is proposed, which uses a new procedure for generating sigma points. Unlike the existing sigma point generation methodologies in engineering where negative probability weights may occur, we develop an algorithm capable of generating sample points that always form a...

Nonlinear estimation based on probabilistic inference forms a core component in most modern GNC systems. The estimator optimally fuses observations from multiple sensors with predictions from a nonlinear dynamic state-space model of the system under control. The current industry standard and most widely used algorithm for this purpose is the extended Kalman filter (EKF). Unfortunately, the EKF ...

w x C y a x f x , 1 ABSTRACT In this paper, we consider chaotic communication schemes using nonlinear filtering techniques. Two different previously proposed Extended Kalman Filter based chaotic schemes are revisited and implemented using the Unscented Kalman Filter. Also, a recently proposed antipodal chaotic communication scheme is implemented with both the Extended Kalman Filter and the Unsc...

In this paper, we consider chaotic communication using nonlinear filtering techniques. The main contribution of the paper is proposing a novel antipodal chaotic communication scheme, which is implemented with both the Extended Kalman Filter and the Unscented Kalman Filter. Two different previously proposed Extended Kalman Filter based chaotic schemes are also revisited and implemented using the...

Despite recent interest in continuous prediction of dimensional emotions, the dynamical aspect of emotions has received less attention in automated systems. This paper investigates how emotion change can be effectively incorporated to improve continuous prediction of arousal and valence from speech. Significant correlations were found between emotion ratings and their dynamics during investigat...

Kalman filtering is a method for estimating state variables of a dynamic systems recursively from noise-contaminated measurements. For systems with nonlinear dynamics, a natural extension of the Linear Kalman Filter (LKF), called Extended Kalman filter (EKF) is used. The Kalman filter represents one of the most popular estimation techniques for integrating signals from navigation systems, like ...