The United States, together with the United Kingdom, signed the Limited Nuclear Test Ban Treaty in 1963. The Comprehensive Nuclear-Test-Ban Treaty was partially ratified by the United Nations General Assembly in 1996. A multifaceted worldwide monitoring network, in which the United States actively participates, continuously monitors treaty compliance. One of the tools this worldwide network uses is atmospheric sampling of radioxenon. During an underground detonation, noble gases, such as xenon, do not react with soil and can escape into the atmosphere. The detection of radioactive xenon in 2006 provided reliable proof of North Korea's underground testing. Because radioactive xenon is required for calibrating the detectors, the synthesis of high-purity radioxenon is of interest. In light of this interest, a team at the Johns Hopkins University Applied Physics Laboratory (APL) developed a novel production pathway for the 135Xe isotope using APL's new linear accelerator facility.
Studies using chemical warfare agents (CWAs) and explosives are dangerous and are therefore conducted only in specialized laboratories with highly controlled conditions and limited accessibility. Obtaining these materials for study is another challenge, as they are tightly regulated. Because of these challenges, simulants-molecules that mimic key characteristics of a specified CWA or explosive but lack toxicity-are often used in testing, sensor development, and decontamination studies. In the past, simulants have generally been selected on the basis of their historical use (with researchers sometimes simply choosing "convenient" materials that happen to provide a detector alarm, for example) rather than rational design. The Johns Hopkins University Applied Physics Laboratory (APL) created a simulant development approach, based on systems engineering concepts, that takes input from relevant parties and is scoped specifically for a project objective. This methodology can be universally applied to any type of simulant and includes down-selection criteria to identify specific simulant candidates that can be verified and validated according to the project's required fidelity. This article describes the development approach, selection process, and demonstrated use cases for both CWAs and energetic materials.
Target tracking is a critical component in defense and airspace protection. To provide awareness of potential enemy threats through target tracking, dynamic states are repeatedly updated based on observations. Because common dynamic models of moving objects typically use Cartesian coordinates, target tracking systems typically use this coordinate system as well. This presents a statistical challenge, however, when observations are recorded with different coordinate systems. This is the case with radar measurements, which use spherical coordinates (range, bearing, and elevation) instead of Cartesian coordinates (x, y, z). The main problem is integrating the statistics of new measurements with a priori state estimates to provide an updated a posteriori estimate. This article focuses on a converted-measurement approach to compute descriptive Cartesian statistics from spherical measurements for updates in a linear tracking system. Converted-measurement tracking, compared with mixed-coordinate tracking, can facilitate multisensor fusion in complex sensor networks. Various converted-measurement methods were evaluated, including Taylor approximations, unscented transforms, and debiased statistical methods, in a simple tracking scenario. Tracking performance varied across these three methods depending on the geometry of the scenario, so users of converted-measurement methods should evaluate the performance of each method for their given domain and application.
The Johns Hopkins University Applied Physics Laboratory (APL) plays a crucial role in helping the United States anticipate, counter, and prevail against chemical, biological, radiological, nuclear, and explosive (CBRNE) threats. APL researchers create innovative technologies and methodologies that enhance national security and provide decision-makers with actionable intelligence in the face of evolving threats. The counterterrorism and homeland security landscape has dramatically changed since the Johns Hopkins APL Technical Digest last published a comprehensive review on these topics in 2003. In the years since, APL's expertise has expanded significantly, integrating advancements in artificial intelligence, data analytics, autonomous systems, and sensor technologies to address increasingly sophisticated CBRNE challenges. This issue highlights APL's latest contributions to detecting, identifying, and mitigating CBRNE threats to strengthen national and global security.
Odor detection canines play a key role in ensuring our nation's security. For more than 15 years, advancement of the community's efficacy and capabilities through the application of interdisciplinary solutions spanning chemistry, biology, data analytics, and engineering. Not only have APL's contributions resulted in strong collaborations across the research space, but they have also directly informed and impacted strategies and capabilities for operational deployments of odor detection canines.
