Shifted Rayleigh Filter Research Articles

Uncertainty zone estimation of angles only tracking in undersea environment

Traditional underwater tracking is a method of estimating the state of a target based on measurements obtained from sensors. Observer uses measurements that are created by the radiation of the target for passive underwater target surveillance. A procedure for estimating the location of a target emitting acoustic signal is introduced. The issue is addressed in cases of known and unknown emitter location determination errors. Zones of uncertainty have been classified as error ellipses and confidence ellipses. Noise continuously corrupts the measurements, resulting a target position uncertainty zone. To improve the accuracy, to find uncertainty zone of target use bearing and elevation. Shifted Rayleigh Filter (SHRF) is a non-linear estimator that is effective for estimating the motion parameters of the target. It is important to know about the convergence of the solution in terms of the reduction of the uncertainty zone under a certain constraint in realistic situations. Utilizing the covariance matrix of SHRF in Monte-Carlo simulation, an attempt is made to reduce and discover the target's 3-dimensional range uncertainty ellipse zone (3DRUEZ). The solution is said to be converged when the 3DRUEZ is less than a certain threshold. In underwater surroundings, the simulation demonstrates that the proposed technique in this paper shows enhanced estimation accuracy and more stable performance than other filtering algorithms.

Gaussian sum state estimators for three dimensional angles-only underwater target tracking problems

Gaussian sum filters are considered to be more accurate in terms of estimation accuracy when compared to the conventional algorithms. In this work, Gaussian sum state estimation algorithms are implemented for three dimensional angles-only target tracking problem. Shifted Rayleigh filter (SRF) has been considered as the most accurate estimation algorithms for bearings-only tracking, with moderate computational load. Therefore, SRF formulated in the Gaussian sum framework is developed for solving three dimensional angles-only target tracking problems. The estimation accuracy of the developed algorithm, and other Gaussian as well as Gaussian sum algorithms is validated in terms of percentage track-loss and root mean square error (RMSE).

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

PurposeMarine 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/approachThis 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).FindingsSHRF 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/valueThe 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.

Prevalence of shifted Rayleigh filter for passive surveillance in underwater

PurposeFrom many decades, bearings-only tracking (BOT) is the interested problem for researchers. This utilises nonlinear filtering methods for state estimation as there is only information about the target, i.e. bearing is a nonlinear measurement. The measurement bearing is tangentially related to the target state vector. There are many nonlinear filtering algorithms developed so far in the literature.Design/methodology/approachIn this research work, the recently developed nonlinear filtering algorithm, i.e. shifted Rayleigh filter (SRF), is applied to BOT.FindingsThe SRF is tested for two-dimensional BOT against various scenarios. The simulation results emphasise that the SRF performs well when compared to the standard nonlinear filtering algorithm, unscented Kalman filter (UKF).Originality/valueSRF utilises the nonlinearities present in the bearing measurement through the use of moment matching. The SRF is able to produce the solution in highly noisy environment, long ranges and high dimension tracking.

Gaussian Sum Shifted Rayleigh Filter for Underwater Bearings-Only Target Tracking Problems

In this paper, a new Gaussian sum (GS) filter with shifted Rayleigh filter (SRF) as proposal has been developed for the bearings-only tracking (BOT) problem. The proposed filter estimates the prior and posterior probability density functions as weighted sum of several Gaussian densities, where the individual Gaussian densities are generated from the SRF. Performance of the proposed filter on the BOT problem is compared with the SRF, the GS sparse-grid Gauss-Hermite filter, and other available GS and quadrature-based filters. Performance of filters is compared in terms of a root-mean-square error (RMSE), track divergence, and computational time. The effect of initial uncertainty, measurement noise covariance, and sampling time on filtering accuracy are also studied. Finally, RMSEs of all the filters are evaluated in comparison with the Cramer-Rao lower bound. From simulation results, it is observed that the performance of the proposed GS-SRF is superior to all other nonlinear filters considered.

Variational Bayesian Based Adaptive Shifted Rayleigh Filter for Bearings-Only Tracking in Clutters.

This paper considers bearings-only target tracking in clutters with uncertain clutter probability. The traditional shifted Rayleigh filter (SRF), which assumes known clutter probability, may have degraded performance in challenging scenarios. To improve the tracking performance, a variational Bayesian-based adaptive shifted Rayleigh filter (VB-SRF) is proposed in this paper. The target state and the clutter probability are jointly estimated to account for the uncertainty in clutter probability. Performance of the proposed filter is evaluated by comparing with SRF and the probability data association (PDA)-based filters in two scenarios. Simulation results show that the proposed VB-SRF algorithm outperforms the traditional SRF and PDA-based filters especially in complex adverse scenarios in terms of track continuity, track accuracy and robustness with a little higher computation complexity.

Consecutive tracking for ballistic missile based on bearings-only during boost phase

This paper proposes a modified centralized shifted Rayleigh filter (MCSRF) algorithm for tracking boost phase of ballistic missile (BM) trajectory with a highly nonlinear dynamical model based on bearings-only. This paper contributes three folds. Firstly, the mathematical model of an MCSRF for multiple passive sensors is derived. Then, minimum entropy based one-dimensional optimization search to adaptively adjust the probability of the different filters for real time state estimation is deployed. Finally, the unscented transform (UT) is introduced to resolve the asymmetric state estimation problem. Simulation results show that the proposed algorithm can consecutively track the BM precisely during the boost phase. In comparison with the unscented Kalman filter (UKF) algorithm, the proposed algorithm effectively reduces the tracking position and velocity root mean square (RMS) errors, which will make more sense for early precision interception.

Shifted Rayleigh filter: a new algorithm for bearings-only tracking

A new algorithm, the "shifted Rayleigh filter," is introduced for two- or three-dimensional bearings-only tracking problems. In common with other "moment matching" tracking algorithms such as the extended Kalman filter and its modern refinements, it approximates the prior conditional density of the target state by a normal density; the novel feature is that an exact calculation is then performed to update the conditional density in the light of the new measurement. The paper provides the theoretical justification of the algorithm. It also reports on simulations involving variants on two scenarios, which have been the basis of earlier comparative studies. The first is a "benign" scenario where the measurements are comparatively rich in range-related information; here the shifted Rayleigh filter is competitive with standard algorithms. The second is a more "extreme" scenario, involving multiple sensor platforms, high-dimensional models and noisy measurements; here the performance of the shifted Rayleigh filter matches the performance of a high-order bootstrap particle filter, while reducing the computational overhead by an order of magnitude.

The Shifted Rayleigh Mixture Filter for Bearings-Only Tracking of Maneuvering Targets

This paper introduces the shifted Rayleigh mixture filter (SRMF), which is based on jump Markov linear systems. The formulation permits the presence of clutter. For bearings-only tracking problems involving maneuvering targets, the conditional density of the target state given the available measurements evolves as a growing mixture of probability density functions associated with a history of manoeuvre "modes." Similar to other "mixture" algorithms, the SRMF approximates this conditional density by a Gaussian mixture of fixed order. Unlike the extended or unscented Kalman filters, the shifted Rayleigh filter incorporates an exact calculation of the posterior density, when the prior is assumed to be Gaussian, given the latest bearings measurement. Computer simulations are provided to demonstrate the performance of the algorithm.

Shifted rayleigh filter: A new algorithm for bearings-only tracking

Shifted rayleigh filter: A new algorithm for bearings-only tracking