Data assimilation in a two-dimensional hydrodynamic model for bays, estuaries and coastal areas is considered. Two different methods based on the Kalman filter scheme are presented. These include (1) an extended Kalman filter in which the error covariance matrix is approximated by a matrix of reduced rank using a square root factorisation (RRSQRT KF), and (2) an ensemble Kalman filter (EnKF) ba...

Different representations to describe noise processes and finding connections or equivalence between them have been part of active research for decades, in particular for linear time-invariant case. In this paper the linear parameter-varying (LPV) setting is addressed; starting with the connection between an LPV state-space (SS) representation with a general noise structure and the LPV-SS model...

SLAM (Simultaneous Localization and Mapping) is a fundamental problem when an autonomous mobile robot explores an unknown environment by constructing/updating the environment map and localizing itself in this built map. The all-important problem of SLAM is revisited in this paper and a solution based on Adaptive Unscented Kalman Filter (AUKF) is presented. We will explain the detailed algorithm...

The conventional Kalman filter assumes a constant process noise covariance according to the system’s dynamics. However, in practice, the dynamics might alter and the initial model for the process noise may not be adequate to adapt to abrupt dynamics of the system. In this paper, we provide a novel informed Kalman filter (IKF) which is informed by an extrinsic data channel carrying information a...

Comparisons of six heating source localisation measurement models are conducted where temperature or ultrasonic time of flight readings provide the measurement update to the extended Kalman filter for estimating the location of a high heat flux spot source on a flat plate. For a particular measurement model, one of two processes are used: 1) directly using the measurements as the measurement ve...

The interval Kalman filter is a variant of the traditional Kalman filter for systems with bounded parametric uncertainty. For such systems, modelled in terms of intervals, the interval Kalman filter provides estimates of the system state also in the form of intervals, guaranteed to contain the Kalman filter estimates of all point-valued systems contained in the interval model. However, for prac...

Two classes of state estimation schemes, variational (4DVar) and ensemble Kalman (EnKF), have been developed and used extensively by the weather forecasting community as tractable alternatives to the standard matrix-based Kalman update equations for the estimation of high-dimensional nonlinear systems with possibly nongaussian PDFs. Variational schemes iteratively minimize a finite-horizon cost...

Ultra-wide band (UWB) communication is one of the most promising technologies for high data rate wireless networks for short range applications. This paper proposes a blind channel estimation method namely IMM (Interactive Multiple Model) Based Kalman algorithm for UWB OFDM systems. IMM based Kalman filter is proposed to estimate frequency selective time varying channel. In the proposed method,...

The problem of distributed Kalman filtering (DKF) for sensor networks is one of the most fundamental distributed estimation problems for scalable sensor fusion. This paper addresses the DKF problem by reducing it to two separate dynamic consensus problems in terms of weighted measurements and inverse-covariance matrices. These to data fusion problems are solved is a distributed way using lowpas...

Compared with loose and tight integration, ultra-tight GPS/INS integration offers improved performance under exposure to high dynamics, jamming and interference by employing the advanced Kalman filter design methods. The ultra-tight configuration delivers unique advantages by aiding the GPS tracking loops with velocity estimates derived from the Kalman Filter. In this manner, only the errors in...