Phases we support in the development chain:
From requirements and preliminary concept evaluation to proof-of-concept test beds and prototypes. All work is protected under NDA and a clearly defined Statement of Work.
Requirements Analysis
The formality level for requirements are tied to the technical maturity of the sensing concepts being explored or applied. Early R&D efforts seeking to understand and exploit a new phenomenology can have more loosely defined requirements. Once basic feasibility of a sensing approach emerges, a critical first step is clearly defining what outcome is sought and how it fits into a future objective. This can be captured as a requirements matrix that may include specific performance metrics required to meet a customer need.
Concept Definition
The feasibility of achieving a set of requirements can be evaluated by coupling the target environment & phenomenology to the metrics to be extracted ( e.g. chemical composition or target/object features for example). Access to previous academic research can play a critical role identifying potentially exploitable features, as does broad knowledge of front end sensing technologies applicable to the problem domain. Sensor concepts and signal extraction methods can then be postulated for further evaluation using analysis, simulations and data sets.
Performance Analysis, Trade Studies, Concept Validation
Once a functional architecture is defined, models can be developed and simulated in typical environments to assess critical performance drivers and to trade off alternate designs. Further confirmation of feasibility requires incorporation of real data sets derived from test beds using prototype sensor hardware. This data is applied to signal conditioning and preliminary exploitation algorithms to understand how well the sensing system could ultimately perform its mission and where key shortcomings currently exist.
Prototype Development
Early prototyping and field test beds perform critical data collections that enable concept validation using sensors exposed to real-world phenomenology. Data is evaluated against models/simulation and provides critical feedback: do we understand the physics involved, can we extract and exploit the information desired, how well does the current processing chain achieve this? These results drive design modification and further evaluation. Field capable prototype units target performance/KPI objectives but typically are not optimized for cost or form factor.