James V. Candy

Bootstrap Particle Filtering

This article provides an overview of nonlinear statistical signal processing based on the Bayesian paradigm. The next-generation processors are well founded on MC simulation-based sampling techniques. The development of the sequential Bayesian processor is reviewed using the state-space models. The popular bootstrap algorithm was outlined and applied to an ocean acoustic synthetic aperture towed array target tracking problem to test the performance of a particle filtering technique.

Dynamic Estimation of the Sound-Speed Profile from Broadband Acoustic Measurements

Global search and more recently adjoint-based inversion methods used in ocean acoustics showed their effectiveness in the estimation of the sound-speed profile (SSP) in water columns of several environments. In the framework of the European Seas Observatory Network (ESONET) an essential part of the research and technology focuses on continuous and long term observations to characterize dynamic ocean processes and monitor the global state of the ocean. Therefore, the development of high performance integrated tools for acoustic inversion is one of the attractive components in this network. For the purpose of efficient data assimilation this paper investigates sequential methods that are able to update sound-speed profile parameters, typically the coefficients of empirical orthogonal functions (EOF), with respect to new incoming acoustic or hydrographic measurements and take into account the seafloor and sub-seafloor acoustic properties in a shallow water environment. A formulation using Kalman filters is suitable for a sequential treatment. This paper investigates the application of two different extensions of the Kalman filter, the extended Kalman filter and the more recent unscented Kalman filter for comparison.

Passive vector geoacoustic inversion in coastal areas using a sequential unscented Kalman filter

An unscented Kalman filter (UKF) for geoacoustic inversion using scalar and vector sound fields created by a passing ship is discussed in this paper. The continuous sound field emitted by a ship of opportunity is processed by the sequential filtering technique to estimate slowly changing environmental properties along the source range. The inversion problem is solved by the UKF with a random-walk parameter model, which is expected to perform well when dealing with highly nonlinear problems. Synthetic geoacoustic inversions are performed using multi-frequency pressure, vertical particle velocity and waveguide impedance (a ratio between pressure and vertical particle velocity) data for the geoacoustic model of a mud environment offshore at the mouth of the Amazon River in Brazil (CANOGA 12). For the preliminary tests, the sound source is composed of a flat spectrum. Numerical results demonstrate that the sequential filtering technique is capable of estimating the evolution of environmental properties along the source range. In practice, ship data have complex time-varying spectral characteristics that can greatly limit the accuracy of broadband or multi-frequency passive applications. Since the vertical waveguide impedance is independent of the source spectral level, it is preferred for environmental characterization by the sound field generated from a ship of opportunity. Because of this independence property, the vertical waveguide impedance is expected to yield a more reliable inversion than that of pressure or vertical particle velocity field.

A Model-Based Processor Design for Smart Microsensor Arrays [Applications Corner]

In this article, we discuss the design of a smart-physics-based processor for microcantilever sensor arrays. The processor is coupled to a microelectromechanical sensor and estimates the presence of critical materials or chemicals in solution. We first briefly present microcantilever sensors and then discuss the microcantilever sensor array design, which consists of the cantilever physics propagation model, cantilever array measurement model, model-based parameter estimator design, and model-based processor (MBP) design. Finally, we end with experimental results and conclusions