The generalized Kalman filter approach to adaptive underwatertarget tracking

The generalized Kalman filtering (GKF) method is applied to underwater target tracking. The proposed GKF is based on the formulation developed by J.C. Lagarias and F. Aminzadeh (1983) establishing a tradeoff between the cost associated with estimation error and the cost related to the lateral discontinuity of the estimates. By assigning proper weights for accuracy and stability in the objective function, the desired balance between accuracy of estimates and lateral continuity is achieved. Computational results illustrate the performance of the technique. Conclusions as to the effects of the accuracy and stability weights are drawn     

Refined autoregressive moving average modeling of underwater heaveprocess

Earlier treatments of the underwater dynamic motion effects including the heave, or vertical dynamic motion, phenomenon relied on frequency response methods and Kalman filtering for the compensation task. An alternative model of the heave process is proposed. The model is based on transforming the underlying time series using exponential operations and then finding autoregressive integrated moving-average (ARIMA) representations of the time series. A refined ARMA model based on modeling of a series of innovations is also proposed. A computational comparison of the performance of two estimators is conducted using a real heave record as a base case. The refined ARMA model gives better results than the other alternative models investigated     

Event enhancement of reflections from subsurface layered media

The problem of obtaining amplitude and delay parameters associated with the response of subsurface layered sedimentation to impulse-type acoustic excitations is considered. We use a linear lossless model which characterizes the subsurface in terms of layerwise homogeneous segments. The parameters of the model are the time delays associated with the wave propagation in the various layers and amplitude parameters which are functions of the reflection coefficients at the interfaces. With knowledge of the travel time in a sediment of rock layer, its thickness can be estimated if the medium velocity is known. This paper extends theory developed by the authors. In particular, a procedure to enhance the detection of reflection events is presented. The procedure utilizes a balance property shown by the derivative of the input waveform signal to obtain an improved display of reflection-event portions of the received signals. Computational results are given in the paper to illustrate the effectivess of the procedure.     

Fair and Andrews's weighting-based IRWLS algorithms for time-delayestimation in underwater target tracking

Underwater target tracking relies on a model relating the target states to time-delay and bearing measurements. This furnishes linearized measurement models. Problems arise due to fitting models using the least-squares procedure, whose success may depend on the assumption that the data noise distribution is Gaussian. For many cases of non-Gaussian errors, performance of the least-squares estimators is far from optimal. Robust regression procedures have been proposed to improve the performance of the least-squares procedures for non-Gaussian errors, and to enhance their performance for the Gaussian errors. Filters for time-delay estimation based on the Fair and Andrews's weighting functions of the iteratively reweighted least-squares method are proposed. Computational results are given to illustrate and compare the performances of the two filters as well as that due to ordinary least-squares filters     

Robust regression-based EKF for tracking underwater targets

In underwater target tracking applications, measurement uncertainty and inaccuracies are usually modeled as additive Gaussian noise. The Gaussian model of noise may not be appropriate in many practical systems. The non-Gaussian noise and the model non-linearity arising in a tracking system will seriously affect the tracking performance. This paper discusses one way to create a robust version of the extended Kalman filter for enhanced underwater target tracking. State estimation in the filter is done through the robust regression approach and Welsch's proposal is used in the regression process. Monte Carlo simulation results with heavy-tailed contaminated observation noise demonstrate the robustness of the proposed estimation procedure     

Compensation for source heave by use of a Kalman filter

This paper presents a procedure for data filtering to compensate for the effects of the towed body dynamics (heave), in shallow marine seismic reflection records. A method to extract an approximate record of the heave contribution to data collected is outlined. The method utilizes the time to water-sediment interface on each acoustic return record to construct the required approximate heave motion record. The frequency response of the heave component record provides the basis for a proposed linear model for the heave motion. A formulation of the heave compensation requirement as a Kalman filtering problem in optimal linear estimation theory is given. A discussion of the computational aspects and practical results are discussed to conclude the paper.