This paper deals with spacecraft autonomous navigation in deep space missions. The considered problem is that of spacecraft localization based on angular measurements. The dynamic model of the spacecraft accounts for several perturbing effects, such as Earth and Moon gravitational field asymmetry and errors associated with the Moon ephemerides. The measurement process is based on elevation and ...

This paper describes the passive emitter localization using Time Difference of Arrival (TDOA) measurements. It investigates various methods for estimating the solution of this nonlinear problem: the Maximum Likelihood Estimation (ML) as a batch algorithm, the Extended Kalman Filter (EKF) as an analytical approximation, the Unscented Kalman Filter (UKF) as a deterministic sampling approach and f...

This paper investigates the use of statistical linearization to improve iterative non-linear least squares estimators. In particular, we look at improving long range stereo by filtering feature tracks from sequences of stereo pairs. A novel filter called the Iterated Sigma Point Kalman Filter (ISPKF) is developed from first principles; this filter is shown to achieve superior performance in ter...

This paper is concerned with filtering nonlinear multivariate time series. A new approximate Bayesian algorithm is proposed which generates sample points and corresponding probability weights that match exactly the predicted values of average marginal skewness and average marginal kurtosis of the unobserved state variables, in addition to matching their mean and the covariance matrix. The perfo...

This work proposes a novel application of existing powerful nonlinear filters, such as the standard Extended Kalman Filter (EKF), some of its variants and the standard Unscented Kalman Filter (UKF), to the estimation of a continuous spatio-temporal field that is spread over a wide area, and hence represented by a large number of parameters when parameterized. We couple these filters with the po...

A novel adaptive unscented Kalman filter (AUKF) based estimation algorithm is proposed for a 3U Cubsat. This small satellite employs a three axis magnetometer and three MEMS gyroscopes as well as three magnetic torque rods and one reaction wheel on the pitch axis. Unlike the existing UKF, in this paper, an n+1 sigma set is used to estimate the nanosatellite attitude instead of 2n + 1 sigma poin...

For the general nonlinear systems, a universal weighted measurement fusion (WMF) algorithm is presented via the Taylor series expansion method. Based on the proposed fusion algorithm and the well-known Unscented Kalman Filter (UKF), the WMF-UKF is presented. It is proven that the proposed WMF-UKF asymptotically approaches to the centralized measurement fusion UKF (CMF-UKF) with the increase of ...

In this paper, we propose an efficient geo-location system for social safety robot using a single base station, where its location is obtained by direction of arrival (DOA) and time of arrival (TOA) of the radio signal. The proposed system requires two reference signals while the conventional systems generally deploy more than three reference signals. For estimating DOA and TOA information toge...

This paper proposes a parameter estimation method for improving the navigation accuracy of an unmanned underwater vehicle, which is equipped with a doppler, a gyro, a depth sensor and a GPS, using an Unscented Kalman Filter (UKF). The error sources are analyzed, and then a kinematical model is constructed accordingly. Using an UKF, the doppler scaling factor and the rotational alignment offset ...

An accurate and low-cost level measurement method based on acoustic resonance is presented. The method is useful in the cases that measurements are noisy which might be due to environment noise or due to low-cost instruments used in the measurement process. An iterative nonlinear filtering algorithm, called the “Unscented Kalman filter” (UKF) has been employed to obtain a good estimate of the n...