This paper proposes a new adaptive extended Kalman filter (AEKF) for a class of nonlinear systems perturbed by noise which is not necessarily additive. The proposed filter is adaptive against the uncertainty in the process and measurement noise covariances. This is accomplished by deriving two recursive updating rules for the noise covariances, these rules are easy to implement and reduce the n...

The purpose of this study is to develop an adaptive unscented Kalman filter (UKF) by tuning the measurement noise covariance. We use the maximum likelihood estimation (MLE) and the covariance matching (CM) method to estimate the noise covariance. The multi-step prediction errors generated by the UKF are used for covariance estimation by MLE and CM. Then we apply the two covariance estimation me...

y(t) = Ax(t) + e(t) (1) where y(t) ∈ Cm×1 and A = A(θ) ∈ Cm×nθ . It is assumed that: • Emitter signals x(t) are random. • Observation vectors {y(t)}t=1,2,... are i.i.d. circular Gaussian random variables with zero mean (, i.e. eiφZ has the same probability distribution as Z for all real φ, see for example [3]). • The emitter signal x(t) and the noise e(t) are uncorrelated which gives that R(θ, ...

To perform any economic management of a petroleum reservoir in real time, a predictable and/or updateable model of reservoir along with uncertainty estimation ability is required. One relatively recent method is a sequential Monte Carlo implementation of the Kalman filter: the Ensemble Kalman Filter (EnKF). The EnKF not only estimate uncertain parameters but also provide a recursive estimat...

The particle filter (PF) is a novel technique that has sufficiently good estimation results for the nonlinear/non-Gaussian systems. However, PF is inconsistent that caused mainly by loss of particle diversity in resampling step and unknown a priori knowledge of the noise statistics. This paper introduces a new modified particle filter called adaptive unscented particle filter (AUPF) to overcome th...

Outdoor applications for small-scale Unmanned Aerial Vehicles (UAVs) commonly rely on Global Positioning System (GPS) receivers for continuous and accurate position estimates. However, in urban areas GPS satellite signals might be reflected or blocked by buildings, resulting in multipath or non-line-of-sight (NLOS) errors. In such cases, additional onboard sensors such as Light Detection and Ra...

In this work the recently developed fuzzy logic-based adaptive Kalman filter (FL-AKF) is used to build adaptive centralized, decentralized, and federated Kalman filters for Adaptive MultiSensor Data Fusion (AMSDF). The adaptation carried out is in the sense of adaptively adjusting the measurement noise covariance matrix of each local FL-AKF to fit the actual statistics of the noise profiles pre...

Spectrum sensing has been put forward to make more efficient use of scarce radio frequency spectrum. The leading eigenvector of the sample covariance matrix has been applied to spectrum sensing under the frameworks of PCA and kernel PCA. In this paper, spectrum sensing with subspace matching is proposed. The subspace is comprised of the eigenvectors corresponding to dominant non-zero eigenvalue...