Shifted Rayleigh filter: a novel estimation filtering algorithm for pervasive underwater passive target tracking for computation in 3D by bearing and elevation measurements

Purpose Marine exploration is becoming an important element of pervasive computing underwater target tracking. Many pervasive techniques are found in current literature, but only scant research has been conducted on their effectiveness in target tracking. Design/methodology/approach This research paper, introduces a Shifted Rayleigh Filter (SHRF) for three-dimensional (3 D) underwater target tracking. A comparison is drawn between the SHRF and previously proven method Unscented Kalman Filter (UKF). Findings SHRF is especially suitable for long-range scenarios to track a target with less solution convergence compared to UKF. In this analysis, the problem of determining the target location and speed from noise corrupted measurements of bearing, elevation by a single moving target is considered. SHRF is generated and its performance is evaluated for the target motion analysis approach. Originality/value The proposed filter performs better than UKF, especially for long-range scenarios. Experimental results from Monte Carlo are provided using MATLAB and the enhancements achieved by the SHRF techniques are evident.

Measure of Nonlinearity With Application to Bearings-Only Target Tracking

Using the recently proposed measure of nonlinearity (MoN), the authors try to find the magnitude of nonlinearity for passive target tracking with bearings-only measurements in underwater environment. The method derived to measure the nonlinearity is completely based on the state covariance matrices of the filters. It is tried to find the allowable magnitude of nonlinearity in terms of MoN with which a filter can perform to estimate the target motion parameters with required accuracy. In this paper, MoN values for different filters are computed for different scenarios. Results obtained in the Monte Carlo simulation are presented.

Attitude dynamics aiding for three-dimensional passive target tracking of strap-down seeker based on instrumental variable Kalman filter

The accuracy of three-dimensional (3D) passive target tracking under strap-down system is usually affected by the measurement accuracy of attitude angular rate and attitude angle. In order to save the problem, a novel 3D passive target tracking method based on instrumental variable Kalman filter (IVKF) aided by the attitude dynamic is proposed. At first, the maneuvering target motion model is established based on the “current” statistical model and the filtering equation of MEMS inertial measurement unit (IMU) is also set up. Then, linearize the nonlinear state equations and replace the nonlinear measurement equations with pseudolinear equations. The 3D pseudolinear Kalman filter (PLKF) algorithm is derived according to the linear Kalman filter (KF). To counter the severe bias problems with PLKF, bias compensation and recursive instrumental variable (IV) methods are considered. In order to enhance observability of the system, a 3D motion tracking sliding-mode guidance law is deduced. Finally, some mathematical simulations were made to verify the effectiveness of the proposed method. The simulation results show the effect of the measurement accuracy and complexity of the algorithm are reduced, which proves the validity of the method.

Application of Spherical-Radial Cubature Bayesian Filtering and Smoothing in Bearings Only Passive Target Tracking

In this paper, an application of spherical radial cubature Bayesian filtering and smoothing algorithms is presented to solve a typical underwater bearings only passive target tracking problem effectively. Generally, passive target tracking problems in the ocean environment are represented with the state-space model having linear system dynamics merged with nonlinear passive measurements, and the system is analyzed with nonlinear filtering algorithms. In the present scheme, an application of spherical radial cubature Bayesian filtering and smoothing is efficiently investigated for accurate state estimation of a far-field moving target in complex ocean environments. The nonlinear model of a Kalman filter based on a Spherical Radial Cubature Kalman Filter (SRCKF) and discrete-time Kalman smoother known as a Spherical Radial Cubature Rauch–Tung–Striebel (SRCRTS) smoother are applied for tracking the semi-curved and curved trajectory of a moving object. The worth of spherical radial cubature Bayesian filtering and smoothing algorithms is validated by comparing with a conventional Unscented Kalman Filter (UKF) and an Unscented Rauch–Tung–Striebel (URTS) smoother. Performance analysis of these techniques is performed for white Gaussian measured noise variations, which is a significant factor in passive target tracking, while the Bearings Only Tracking (BOT) technology is used for modeling of a passive target tracking framework. Simulations based experiments are executed for obtaining least Root Mean Square Error (RMSE) among a true and estimated position of a moving target at every time instant in Cartesian coordinates. Numerical results endorsed the validation of SRCKF and SRCRTS smoothers with better convergence and accuracy rates than that of UKF and URTS for each scenario of passive target tracking problem.