To protect soft targets and crowded places, we must think of checkpoints and beyond; in many scenarios, checkpoints are neither possible nor desirable. We aim to address major gaps in DHS’s detection capabilities by creating systems that track a threat signature to its source. To achieve this, two novel vapor sensors are proposed: a quantum cascade laser (QCL), mid-infrared (MIR) laser spectroscopy, and a reflected light telescope, all aimed at the vapor plume.
The first sensor is a 24/7 continuously monitoring wide-area sensor capable of statically sampling the atmosphere surrounding soft targets and crowded spaces using mid-infrared (MIR) lasers coupled to back-reflected light devices. The second is a dynamic MIR laser source 69 and detector mounted on unmanned ground/air vehicles to confirm readings from close-range sensors. Both laser sensing schemes will be aimed at vapor plumes of chemical and biological threats (CBTs). MIR laser point sensing allows the user to detect threats using a non-invasive, eye-safe, real-time, and no-contact human-threat analysis technology that marks the upcoming security threat. Threats may be challenging to identify and locate, whether they be explosives (HE & HME), toxic industrial chemicals (TICs), insecticides/herbicides, narcotics, chem-bio threats (CBTs), etc.,
Developing unmanned vehicles mounted with chemical sensing platforms to identify the threat in real-time and locate its source will be an excellent tool for the DHS and defense agencies. The wide-area, continuous monitoring device will provide a detection vector to guide the unmanned vehicles toward the potential target. Back-reflection optical devices will be strategically located to cover as much head space as possible within the 0.1-1 km range (or >). These sensors require expanding their threat recognition libraries, training for detection at a distance under various conditions, and discriminating against interferents. This process will be assisted by multivariate analysis (MVA or Chemometrics) in the data analysis process, and later in the project, machine learning (ML) and artificial intelligence (AI) routines will be introduced. Mounting the QCL/MCT on a UV and detecting threats in an open environment will be addressed in subsequent years.