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 Defense Advanced Research Projects Agency SIGMA+ program developed a persistent, real-time, early warning and detection system for the full spectrum of chemical, biological, radiological, nuclear, and explosive weapon of mass destruction threats at the city to region scale. In support of this program, and leveraging technical expertise in modeling and simulation, applied mathematics, and epidemiology, the Johns Hopkins University Applied Physics Laboratory (APL) characterized and quantified the impact a wearables-based human sentinel network would have on the ability to provide advanced detection of a naturally occurring or intentional biothreat event. Modeling results demonstrate that instrumenting as few as 5% of the population could advance detection of seasonal influenza by 5–14 days and an anthrax attack by ~1 day as compared with traditional public health surveillance. Early detection and geolocation of individuals exposed to biological threats enables timelier and more effective biothreat countermeasures and mitigation strategies.

Biothreat detection strategies have historically focused on cheap, specific, and deployable assays that detect a small but specific nucleic acid or protein component of a threat organism. Genomic sequencing technologies that have emerged over the past 15 years are poised to find their place in the biothreat detection tool kit for military and civilian use. Here we describe efforts to compare and contrast sequencing to traditional polymerase chain reaction (PCR) assays for diagnostics and detection of biothreat agents of concern in military applications. We show that after direct spiking of human blood and serum with biothreat simulants, agnostic sequencing can achieve detection. However, for known agents, PCR is still superior in terms of speed, cost, scale, and reliability for military applications. Although PCR should still be the first choice for diagnostics and detection when an agent is known or suspected, for unknown agents, agnostic sequencing can be a powerful addition to identify causative agents in soils, aerosols, and biothreats in patient samples. APL developed and conducted this work for the Department of Defense to address the basic question of when to use PCR versus when to use sequencing for field-forward infectious disease diagnostics and environmental detection.