4D TECH SOLUTIONS, LLC — Department of Homeland Security SBIR Phase I: DHS201-008
4D TECH SOLUTIONS, LLC — SBIR Phase I award from Department of Homeland Security.
Phase I SBIR feasibility signal
- Phase I awards fund proof-of-concept work. For capture teams, they mark early interest from Department of Homeland Security in a technical approach.
- Watch for Phase II follow-ons from the same firm/topic family — that conversion path is where budgets and transition pressure rise.
- Obligated amount $149,522. Cross-check similar awards in the same agency and technology tags for going-rate context.
- Topic code DHS201-008 links this award to a solicitation family — search the same topic stem for incumbents and recompete timing.
- Amount
- $149,522
- Agency
- Department of Homeland Security
- Program / Phase
- SBIR · Phase I
- Topic
- DHS201-008
- Solicitation
- 20.1
- NAICS
- —
- Place of performance
- WV
- Period
- 2020-05-18 → 2020-11-17
Description
4D Tech Solutions, Inc. will develop a micro-electromechanical system (MEMS) mirror-based light detection and ranging (LiDAR) sensor that can be used for the detection, tracking, and identification of small unmanned aerial vehicles (SUAVs) in urban canyons.The system will have superior detection, tracking and identification capability as a result of the high point density within the scanned field of view and the low beam divergence.The system offers may benefits over existing detection systems as it provides positive range and bearing information in a cluttered urban environment.This range and bearing information will be used for precision tracking.The system will operate both day and night as the pulsed laser provides target illumination.The LiDAR system will be lightweight and easy to deploy to allow for a broad range of fixed and mobile installation techniques.The high-resolution point cloud generation capabilities will provide the data density needed for the detection, tracking, and identification of closely spaced SUAVs that may be operating in unison.Threat identification is made possible through the analysis of received pulse information such as return pulse intensity and the trajectory characteristics.Machine learning techniques are employed to minimize or eliminate false positive and false negative detections.