Identifying high-density fissile materials inside cargo shipping containers is a current national security need. We propose using the geophysical technique of gravity gradiometry to quickly and accurately image cargo containers to detect high-density materials. To obtain realistic images with sharp boundaries, robust estimators are applied to the model objective function of the inversion operator and coupled with a Huber norm to provide stable numerical computation. Assuming a realistic data acquisition pattern requiring a reasonable amount of time, we show it is possible to identify high-density materials with a volume of 15 cm³ Finally, we provide a recovered density threshold to suggest the presence of high-density materials from an arbitrary recovered density model.

The possibility of the detection of <> presence inside sea containers has been evaluated. The method proposed for detection of simultaneous presence of explosive and radioactive material inside the vehicle or container makes use of two active sensors (x-ray and neutron sensor) and one passive (neutron and gamma ray sensor). In the proposed system a commercial imaging device based on the x-ray radiography performs a fast scan of the container, identifies a "suspect" region and provides its coordinates to the neutron based device for the final "confirmatory" inspection. In the neutron sensor 14 MeV neutron beam defined by the detection of the associated alpha particles is being used. The object's nature is determined from passive, and neutron induced, gamma energy spectra measurements. Time-of-flight and gamma energy spectra have been measured for different explosives and simulants as well as for the number of radioactive materials hidden inside the vehicle and/or container.

While there has been important research and development in the area of smart container technologies, no system design methodologies have yet emerged for integrating this technology into the existing shipping and law enforcement infrastructure. A successful deployment of smart containers requires a precise understanding of how to integrate this new technology into the existing shipping and law enforcement infrastructure, how to establish communication interoperability, and how to establish procedures and protocols related to the operation of smart containers. In addition, this integration needs to be seamless, unobtrusive to commerce, and cost-effective. In order to address these issues, we need to answer the following series of questions: 1) Who will own and operate the smart container technology; 2) Who will be responsible for monitoring the smart container data and notifying first responders; 3) What communication technologies currently used by first responders might be adopted for smart container data transmission; and 4) How will existing cargo manifest data be integrated into smart container data. In short, we need to identify the best practices for smart container ownership and operation. In order to help provide answers to these questions, we have surveyed a sample group of representatives from law enforcement, first responder, regulatory, and private sector organizations. This paper presents smart container infrastructure best practices recommendations obtained from the results of the survey.

This paper presents a concept for protecting ships, harbors, pipes or other assets from unfriendly swimmers, using directed energy from an underwater sparker that simultaneously emits intense pulses of light and sound. This concept is based on patented sparkers and reflectors being developed for Navy and commercial applications. Sparkers employ high voltage pulses to produce a plasma discharge between electrodes in water. The discharge generates a high-pressure shock wave and the plasma emits a light pulse. Depending on sparker design, the peak pressure and light power are typically thousands of pounds per square inch and tens of megawatts, respectively, in the vicinity of the sparker. Sparkers can have reflectors to direct and focus the light and sound to specified areas. The sparker system could be deployed in conjunction with a sensor system and activated remotely when the sensor indicates a possible unfriendly swimmer. The intensity of pressure and light pulses decreases with distance from the sparker. Thus, a diver approaching an operating sparker system would be first hailed and/or warned. As the diver continued toward the sparker, the pressure and light intensity would increase to the minor discomfort level and, closer in, to severe disorientation. The sparker systems can have variable intensity so that, for instance, in a closed-loop feedback system with a diver detection system, the output could start at a low level for hailing/warning and be increased if divers continue toward the sparker.

Stevens Institute of Technology is performing research aimed at determining the acoustical parameters that are necessary for detecting and classifying underwater threats. This paper specifically addresses the problems of passive acoustic detection of small targets in noisy urban river and harbor environments. We describe experiments to determine the acoustic signatures of these threats and the background acoustic noise. Based on these measurements, we present an algorithm for robustly discriminating threat presence from severe acoustic background noise. Measurements of the target's acoustic radiation signal were conducted in the Hudson River. The acoustic noise in the Hudson River was also recorded for various environmental conditions. A useful discriminating feature can be extracted from the acoustic signal of the threat, calculated by detecting packets of multi-spectral high frequency sound which occur repetitively at low frequency intervals. We use experimental data to show how the feature varies with range between the sensor and the detected underwater threat. We also estimate the effective detection range by evaluating this feature for hydrophone signals, recorded in the river both with and without threat presence.

In August 2005, numerous test events were conducted in Narragansett Bay (under adverse, moderate, and high signal-tonoise ratio (SNR) conditions) to validate shallow-water acoustic-based detection, localization, and ranging algorithms against surface craft and divers. These measurements were completed at the Naval Undersea Warfare Center Division Newport's Broadband Ocean Acoustic Laboratory, which is a shallow-water development facility for evolving acoustic and light-based technologies that are of interest to the U.S. Navy in areas such as Force Defense and Port and Harbor Security. It is shown that relatively common ambient environmental conditions in Narragansett Bay (such as wind speeds greater than 15 knots) create adverse acoustic conditions and generally poor target detection performance. As expected, the acoustic-based algorithms performed well at moderate to high values of SNR.

SeeCoast extends the US Coast Guard Port Security and Monitoring system by adding capabilities to detect, classify, and track vessels using electro-optic and infrared cameras, and also uses learned normalcy models of vessel activities in order to generate alert cues for the watch-standers when anomalous behaviors occur. SeeCoast fuses the video data with radar detections and Automatic Identification System (AIS) transponder data in order to generate composite fused tracks for vessels approaching the port, as well as for vessels already in the port. Then, SeeCoast applies rule-based and learning-based pattern recognition algorithms to alert the watch-standers to unsafe, illegal, threatening, and other anomalous vessel activities. The prototype SeeCoast system has been deployed to Coast Guard sites in Virginia. This paper provides an overview of the system and outlines the lessons learned to date in applying data fusion and automated pattern recognition technology to the port security domain.

Millions of citizens live and work in the dangerous proximity of chemical plants, at ports and along waterways, which are under-protected and whose security is under-regulated, according to findings of the Congressional Research Service (CRS). There is a new and intense focus on the security of the nation's critical infrastructure. Thanks to recent philosophy and policy shifts within our federal government, the alarming situations in which we find ourselves will be mitigated somewhat a) by setting priorities based on proper threat analysis that considers event likelihoods and consequential impacts, and b) by employing effective systems design and engineering that will make it possible to address the highest priority threats with affordable solutions. It is the latter concern that we address, especially as it is relates to design and engineering of solutions for maintaining vigilance night and day. We begin by reviewing the nature of the facilities we wish to protect, our assumptions, and an accepted framework for analysis. Next we outline a hypothetical design case involving a representative facility and a plausible design basis threat. We then derive requirements for surveillance and examine the interrelationships among key design variables. Finally, we describe a solution involving multiple sensor types coupled with intelligent video. The end result is a significant increase in interdiction probability with a minimum of required assets.

This paper overviews a recent advancement in computer simulating of the performance of lidar sounding and imaging systems. A new iterative technique to retrieve the inherent optical characteristics from lidar waveforms is presented. A few approaches needed for simulation of the high spatial resolution 3D imaging in the surf zone and very shallow waters (regard to the forward pulse stretching, the depth correlation of the random realizations of the background images, including elevations in the sea surface model) as well as modeling of optical properties of bubbles and sediments are briefly discussed.

Optical imaging in turbid ocean water is a challenge due to the high probability that light will scatter multiple times as it propagates to and from the object of interest. Techniques have been developed to suppress the contribution from scattered light and increase the image contrast, such as those using a pulsed source with a gated receiver or a modulated source with a coherent RF receiver. While improving the amplitude contrast of underwater images, these two approaches also have the capability of providing target range information. The effectiveness of each approach for both 2D and 3D imagery depends highly on the turbidity of the intervening water medium. This paper describes a system based on the optical modulation approach, the Frequency Agile Modulated Imaging System (FAMIS), and the techniques that have been developed to improve both amplitude and range imaging in turbid water.

This work describes a visualization tool and sensor testbed that can be used for assessing the performance of both instruments and human observers in support of port and harbor security. Simulation and modeling of littoral environments must take into account the complex interplay of incident light distributions, spatially correlated boundary interfaces, bottom-type variation, and the three-dimensional structure of objects in and out of the water. A general methodology for a two-pass Monte Carlo solution called Photon Mapping has been adopted and developed in the context of littoral hydrologic optics. The resulting tool is an end-to-end technique for simulating spectral radiative transfer in natural waters. A modular design allows arbitrary distributions of optical properties, geometries, and incident radiance to be modeled effectively. This tool has been integrated as part of the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model. DIRSIG has an established history in multi and hyperspectral scene simulation of terrain targets ranging from the visible to the thermal infrared (0.380 - 20.0 microns). This tool extends its capabilities to the domain of hydrologic optics and can be used to simulate and develop active/passive sensors that could be deployed on either aerial or underwater platforms. Applications of this model as a visualization tool for underwater sensors or divers are also demonstrated.

The Autonomous Marine Optical System (AMOS) measures remote sensing reflectance (R_rs) above the water surface and subsurface optical properties (irradiance at depth, beam attenuation, chlorophyll fluorescence, and light backscattering) at predetermined times throughout the day. Data are transmitted back by radio to a networked archival and processing station. AMOS was created to routinely monitor the optical properties of near-surface waters, and make those measurements available to researchers over an Ethernet connection with minimal delay. The Rrs measurements can be used not only to validate satellite and airborne remote sensing imagery, but also to be combined with the in situ measurements so that other water column properties can be estimated. The performance of visible and machine-aided hull inspection is strongly affected by the optical properties of the water. AMOS estimates of these optical properties can be used by optical models to predict both subsurface visibility and the amount of ambient light beneath ships at port inspection sites. An example of the application of an inverse hyperspectral Rrs model to AMOS data from the Port of St. Petersburg (FL) is shown to accurately estimate light absorption due to phytoplankton and colored dissolved organic matter (CDOM), and backscattering due to particles.

Hyperspectral imagery is a powerful sensing technology for quantitative monitoring of coastal environments. In this paper, we present an algorithm to retrieve water optical properties, bathymetry, and bottom albedo using nonlinear optimization techniques. The proposed method combines the Lee semianalytical model, which relates the quantities of interest to the measured remote sensing reflectance, with a modified version of the Goodman linear mixing model for analysis of the bottom albedo. The estimation problem is posed as a nonlinear least squares problem, where the fractional abundances of the mixing model are linear and the optical properties and bathymetry are nonlinear. A simple two-stage Gauss-Seidel optimization algorithm is employed to compute the estimates and take full advantage of the problem structure. We use both simulated and AVIRIS hyperspectral imagery to compare the combined modeling approach with the Lee approach for retrieval of optical properties and bathymetry and with the unmixing approach of Goodman for determining bottom fractional composition. Results show that the proposed retrieval approach generally produces improved estimates of water optical properties, bathymetry and bottom composition but at a significantly higher computational cost. Results also indicate that although the approach is limited in its capacity to resolve bottom composition as a function of increasing depth and water turbity, it retains a robust capability for estimating water optical properties, even in unfavorable conditions for retrieving bathymetry and bottom albedo.

The NATO Security Through Science Program and the Defence Investment Division requested and sponsored the organization of a NATO Advanced Research Workshop (ARW) on the topic of Data Fusion Technologies for Harbour Protection, which was held June 27-July 1, 2005 in Tallinn, Estonia. The goal of the workshop was to help knowledge exchange between the technology experts and the security policy makers for a better understanding of goals, functions and information requirements of the decision makers as well as the way the data fusion technology can help enhancing security of harbours. In addition to presentations by experts from the research community on detection and fusion technologies as well as in practice and policy the workshop program included daily breakout sessions, in which the participants were given an opportunity to brainstorm on the topics of the workshop in interdisciplinary smaller teams. The working groups: (i) chose a scenario, including threat stages, threat types, threat methods and ranges, and response constraints due to the particular harbour environment; then (ii) identified: (a) requirements (objectives, functions and essential elements of information); (b) technologies (available and future); (c) information available and necessary through sensors and other sources, as agencies and jurisdiction; (d) methods: detection, identification, situation assessment, prediction. This paper describes the main issues and proposed approaches that were identified by the working groups.

This document presents a case for applying multiple sources of intelligence and various fusion methodologies to the problem of providing maritime domain awareness for in both the open ocean and for the areas outside of ports and harbors.