Particle filter is proposed for tracking a torpedo using bearings-only measurements when torpedo is attacking an ownship. Towed array is used to generate torpedo bearing measurements. Ownship evasive maneuver is used for observability of the bearings-only process. Particle filter combined with Modified Gain Bearings-Only Extended Kalman Filter is used to estimate torpedo motion parameters, whic...

A nonlinear filter called the iterated modified gain extended Kalman filter (IMGEKF) is presented in this paper. This filter uses bearings only measurements to estimate the target state in passive target tracking scenario. This work combines the MGEKF and the iteration method. The filter utilizes the updated state to re-linearize the measurement equation. Then the proposed work is tested in a t...

[l] R. E. Kalman, “A new approach to linear filtering and prediction problems,” J. Basic Eng., Tram. ASME, pp. 35-45, Mar. 1960. [2] C. K. Chui and G. Chen, Kalrnun Filtering with Real-Time Applications. New York: Springer-Verlag. 1987. [3] L. Ljung, “Asymptotic behavior of the extended Kalman filter as a parameter estimator for linear systems,” IEEE Trans. Automat. Conrr., vol. 24, no. 1. pp. ...

This paper is concerned with the filtering problem for both discrete-time stochastic linear (DTSL) systems and discrete-time stochastic nonlinear (DTSN) systems. In DTSL systems, an linear optimal filter with multiple packet losses is designed based on the orthogonal principle analysis approach over unreliable wireless sensor networks (WSNs), and the experience result verifies feasibility and e...

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

This paper introduces a state estimation framework for legged robots that allows estimating the full pose of the robot without making any assumptions about the geometrical structure of its environment. This is achieved by means of an Observability Constrained Extended Kalman Filter that fuses kinematic encoder data with on-board IMU measurements. By including the absolute position of all footho...

In this work, we propose a distributed adaptive high-gain extended Kalman filtering approach for nonlinear systems. Specifically, we consider a class of nonlinear systems that are composed of several subsystems interacting with each other via their states. In the proposed approach, an adaptive high-gain extended Kalman filter is designed for each subsystem. The distributed Kalman filters commun...

In this paper a method to account for model error due to unresolved scales in sequential data assimilation, is proposed. An equation for the model error covariance required in the extended Kalman filter update is derived along with an approximation suitable for application with large scale dynamics typical in environmental modeling. This approach is tested in the context of a low order chaotic ...

This correspondence describes a method for nonlinear filtering based on an adaptive observer, which guarantees the local stability of the linearised error system. Filter gain selection is by use of a fake algebraic Riccati equation. The design procedure attempts to produce a stable filter at the expense of optimality. This contrasts with the extended Kalman filter (EKF) which attempts to preser...

In this correspondence, the authors develop a novel method based on spherical radial cubature and Gauss–Laguerre quadrature rule for non-linear state estimation problems. The proposed filter, referred as cubature quadrature Kalman filter (CQKF) would be able to overcome inherent disadvantages associated with the earlier reported cubature Kalman filter (CKF). The theory and formulation of CQKF h...