Software Modernization, AFC, Phase I

Algorithms for Modular Remote Expendable Sensor Array

Release Date: 06/11/2024
Solicitation: 24.4
Open Date: 06/26/2024
Topic Number: A244-049
Application Due Date: 07/30/2024
Duration: 6 months
Close Date: 07/30/2024
Amount Up To: $250,000

Objective

Develop and mature decision logic and signal processing approaches for modular, configurable, remotely deployable sensing capabilities surveillance, targeting, or chemical hazard sensing.

Description

Maneuver elements require situational understanding of adversary actions and the threat conditions in their areas of operation. Friendly forces appreciate limited remote sensing capabilities for remote targeting of adversary assets and chemical hazard threats along routes, zones, and areas of interest throughout their battlespace.

Army elements can leverage miniature, low-cost microelectronics-based sensing technology to afford prompt detection of adversary assets and hostile activities to include the possible use of chemical warfare agents to inform long-range fires, effect risk-based maneuver decisions, and avoid chemical hazards.

Advances in microelectronics technology has increasingly enabled functionality and performance in the detection of hazardous chemicals and adversary equipment and actions to include distinguishing between vehicle types and decoys/interferents.

The Army Research Laboratory and the Chemical Biological Center have developed a modular system architecture that facilitates the demonstration of remote, air-dropped sensor arrays that incorporate interchangeable sensing modules, enabling the interchange of the sensing and analysis component of the deployable array system while managing the communications and networking functions as well as supplying a power source for each sensing node.

The industry should focus the development effort on the decision logic at the sensor node (spoke) as well as at the sensor array fusion node (hub) to reduce raw data at the sensor array edge into actionable decision supportive information.

Phase I

Develop a conceptual analytical approach for multimodal detection of military threats. The analytics would be hosted on a remote wireless sensor network consisting of multiple multi-modal sensor platforms connected wirelessly to a gateway node.

This would allow sharing of data between sensor platforms and the gateway (i.e., a two-tiered hierarchical architecture is possible – though not mandatory). The sensor platforms are assumed to contain multi-modal sensing (e.g., seismic, magnetic field, acoustic, electric field, and chemical sensors able to detect chemical warfare agents including G-, V-, H-, L-, A- series).

The sensor array is assumed to haul data via limited data rate wireless means, and signal processing may but does not necessarily be executed in two tiers (sensor edge vs. fusion hub). The sensor network should detect, track, classify and geolocate tactical military vehicles as well as chemical warfare agents. The combined use of geophysical and chemical sensors to improve probability of detection is highly desired.

The sensor platforms are constrained, consisting of microcontrollers with no operating system (OS) and the gateway platform contains slightly more processing capacity (low-cost microprocessor with OS). Performers will be supplied with characterized sensor data for each of the mentioned sensing modalities in response to various environmental stimuli along with supporting ground truth/referee data.

Constraints on the processor should be considered in the analysis, to include limitations on size, weight, and power consumption. The microsensor arrays should be assumed to operate on limited capacity embedded microcontrollers/application specific integrated circuits with no onboard operating system and minimal communications data rates.

Phase II

Develop and demonstrate a prototype set of multi-modal algorithms on representative hardware platforms. The system will first be evaluated in a laboratory setting using government furnished data (i.e., recorded target data) and then outdoors against live threats (system need not be ruggedized). Standards for interoperability will also be furnished by the government along with technical assistance for implementation and integration.

The government will also provide technical assistance and guidance with wireless communications. The government will also provide recommendations for selection of processing platforms and transducers. The prototype system should contain at least six sensor platforms and a gateway. The test range, targets, and CWA simulants will be provided by the government.

Offerors should incorporate one or more field trial events in their proposed program of work per year over the 24-month Phase II period of performance. The government will identify one or more test opportunities per year over the course of the Phase II execution period. Offerors should afford flexibility in the specific time window over which a test event may be programmed.

Phase III

Refine and ruggedize the system and integrate into a representative Army network. Establish a quality assurance procedure to demonstrate cyber and information assurance reliability of the Phase III performance will likely involve the development of non-recurring engineering (NRE) for the production of consistent and reliable software products.

Support a program office with developmental and operational testing and engagement events as opportunities present. Demonstrate the “as published” sensitivity of the modular sensor array against representative adversary movements of personnel or equipment, vehicle types, chemical hazards including G-, V-, H-, L-, A- series threat agents, and objectively demonstrate warning response reliability and performance.

The starting Technology Readiness Level (TRL) on completion of the SBIR Phase III execution Period of Performance should be TRL6 or greater. Develop additional commercial products based on the final integrated system and pursue appropriate demonstration and testing opportunities.

Submission Information

For more information, and to submit your full proposal package, visit the DSIP Portal.

SBIR|STTR Help Desk: usarmy.sbirsttr@army.mil

A244 PHase I

References:

  • Brommer, C., Jung, R., Steinbrener, J., & Weiss, S. (2020). MaRS: A modular and robust sensor-fusion framework. IEEE Robotics and Automation Letters, 6(2), 359-366.;
  • Allak, E., Jung, R., & Weiss, S. (2019, November). Covariance pre-integration for delayed measurements in multi-sensor fusion. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 6642-6649). IEEE.;
  • Fakoorian, S., Otsu, K., Khattak, S., Palieri, M., & Agha-mohammadi, A. A. (2022, September). ROSE: Robust State Estimation via Online Covariance Adaption. In The International Symposium of Robotics Research (pp. 452-467). Cham: Springer Nature Switzerland.;
  • Scheiber, M., Fornasier, A., Jung, R., Böhm, C., Dhakate, R., Stewart, C., & Brommer, C. (2022). CNS Flight Stack for Reproducible, Customizable, and Fully Autonomous Applications. IEEE Robotics and Automation Letters, 7(4), 11283-11290.;
  • Jung, R., & Weiss, S. (2021). Modular Multi-Sensor Fusion: A Collaborative State Estimation Perspective. IEEE Robotics and Automation Letters, 6(4), 6891-6898.;
  • Schofield, A., Bentz, M., Fisher, K., Raquet, J., & Kauffman, K. (2023, June). ASPN 2023: your community-developed PNT standard. In Open Architecture/Open Business Model Net-Centric Systems and Defense Transformation 2023 (Vol. 12544, pp. 142-147). SPIE.;
  • Donavanik, D., Hardt-Stremayr, A., Gremillion, G., Weiss, S., & Nothwang, W. (2016, May). Multi-sensor fusion techniques for state estimation of micro air vehicles. In Micro-and Nanotechnology Sensors, Systems, and Applications VIII (Vol. 9836, pp. 302-317). SPIE.;
  • Nothwang, W. D., Gremillion, G. M., Donavanik, D., Haynes, B. A., Atwater, C. S., Canady, J. D., … & Marathe, A. R. (2016, August). Multi-sensor fusion architecture for human-autonomy teaming. In 2016 Resilience Week (RWS) (pp. 166-171). IEEE.;
  • Koksalmis, E., Kabak, Ö. (2020, July) Sensor fusion based on Dempster-Shafer theory of evidence using a large scale group decision making approach, In International Journal of Intelligent Systems, 35(7) (pp. 1126-1162). https://doi.org/10.1002/int.22237

Objective

Develop and mature decision logic and signal processing approaches for modular, configurable, remotely deployable sensing capabilities surveillance, targeting, or chemical hazard sensing.

Description

Maneuver elements require situational understanding of adversary actions and the threat conditions in their areas of operation. Friendly forces appreciate limited remote sensing capabilities for remote targeting of adversary assets and chemical hazard threats along routes, zones, and areas of interest throughout their battlespace.

Army elements can leverage miniature, low-cost microelectronics-based sensing technology to afford prompt detection of adversary assets and hostile activities to include the possible use of chemical warfare agents to inform long-range fires, effect risk-based maneuver decisions, and avoid chemical hazards.

Advances in microelectronics technology has increasingly enabled functionality and performance in the detection of hazardous chemicals and adversary equipment and actions to include distinguishing between vehicle types and decoys/interferents.

The Army Research Laboratory and the Chemical Biological Center have developed a modular system architecture that facilitates the demonstration of remote, air-dropped sensor arrays that incorporate interchangeable sensing modules, enabling the interchange of the sensing and analysis component of the deployable array system while managing the communications and networking functions as well as supplying a power source for each sensing node.

The industry should focus the development effort on the decision logic at the sensor node (spoke) as well as at the sensor array fusion node (hub) to reduce raw data at the sensor array edge into actionable decision supportive information.

Phase I

Develop a conceptual analytical approach for multimodal detection of military threats. The analytics would be hosted on a remote wireless sensor network consisting of multiple multi-modal sensor platforms connected wirelessly to a gateway node.

This would allow sharing of data between sensor platforms and the gateway (i.e., a two-tiered hierarchical architecture is possible – though not mandatory). The sensor platforms are assumed to contain multi-modal sensing (e.g., seismic, magnetic field, acoustic, electric field, and chemical sensors able to detect chemical warfare agents including G-, V-, H-, L-, A- series).

The sensor array is assumed to haul data via limited data rate wireless means, and signal processing may but does not necessarily be executed in two tiers (sensor edge vs. fusion hub). The sensor network should detect, track, classify and geolocate tactical military vehicles as well as chemical warfare agents. The combined use of geophysical and chemical sensors to improve probability of detection is highly desired.

The sensor platforms are constrained, consisting of microcontrollers with no operating system (OS) and the gateway platform contains slightly more processing capacity (low-cost microprocessor with OS). Performers will be supplied with characterized sensor data for each of the mentioned sensing modalities in response to various environmental stimuli along with supporting ground truth/referee data.

Constraints on the processor should be considered in the analysis, to include limitations on size, weight, and power consumption. The microsensor arrays should be assumed to operate on limited capacity embedded microcontrollers/application specific integrated circuits with no onboard operating system and minimal communications data rates.

Phase II

Develop and demonstrate a prototype set of multi-modal algorithms on representative hardware platforms. The system will first be evaluated in a laboratory setting using government furnished data (i.e., recorded target data) and then outdoors against live threats (system need not be ruggedized). Standards for interoperability will also be furnished by the government along with technical assistance for implementation and integration.

The government will also provide technical assistance and guidance with wireless communications. The government will also provide recommendations for selection of processing platforms and transducers. The prototype system should contain at least six sensor platforms and a gateway. The test range, targets, and CWA simulants will be provided by the government.

Offerors should incorporate one or more field trial events in their proposed program of work per year over the 24-month Phase II period of performance. The government will identify one or more test opportunities per year over the course of the Phase II execution period. Offerors should afford flexibility in the specific time window over which a test event may be programmed.

Phase III

Refine and ruggedize the system and integrate into a representative Army network. Establish a quality assurance procedure to demonstrate cyber and information assurance reliability of the Phase III performance will likely involve the development of non-recurring engineering (NRE) for the production of consistent and reliable software products.

Support a program office with developmental and operational testing and engagement events as opportunities present. Demonstrate the “as published” sensitivity of the modular sensor array against representative adversary movements of personnel or equipment, vehicle types, chemical hazards including G-, V-, H-, L-, A- series threat agents, and objectively demonstrate warning response reliability and performance.

The starting Technology Readiness Level (TRL) on completion of the SBIR Phase III execution Period of Performance should be TRL6 or greater. Develop additional commercial products based on the final integrated system and pursue appropriate demonstration and testing opportunities.

Submission Information

For more information, and to submit your full proposal package, visit the DSIP Portal.

SBIR|STTR Help Desk: usarmy.sbirsttr@army.mil

References:

  • Brommer, C., Jung, R., Steinbrener, J., & Weiss, S. (2020). MaRS: A modular and robust sensor-fusion framework. IEEE Robotics and Automation Letters, 6(2), 359-366.;
  • Allak, E., Jung, R., & Weiss, S. (2019, November). Covariance pre-integration for delayed measurements in multi-sensor fusion. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 6642-6649). IEEE.;
  • Fakoorian, S., Otsu, K., Khattak, S., Palieri, M., & Agha-mohammadi, A. A. (2022, September). ROSE: Robust State Estimation via Online Covariance Adaption. In The International Symposium of Robotics Research (pp. 452-467). Cham: Springer Nature Switzerland.;
  • Scheiber, M., Fornasier, A., Jung, R., Böhm, C., Dhakate, R., Stewart, C., & Brommer, C. (2022). CNS Flight Stack for Reproducible, Customizable, and Fully Autonomous Applications. IEEE Robotics and Automation Letters, 7(4), 11283-11290.;
  • Jung, R., & Weiss, S. (2021). Modular Multi-Sensor Fusion: A Collaborative State Estimation Perspective. IEEE Robotics and Automation Letters, 6(4), 6891-6898.;
  • Schofield, A., Bentz, M., Fisher, K., Raquet, J., & Kauffman, K. (2023, June). ASPN 2023: your community-developed PNT standard. In Open Architecture/Open Business Model Net-Centric Systems and Defense Transformation 2023 (Vol. 12544, pp. 142-147). SPIE.;
  • Donavanik, D., Hardt-Stremayr, A., Gremillion, G., Weiss, S., & Nothwang, W. (2016, May). Multi-sensor fusion techniques for state estimation of micro air vehicles. In Micro-and Nanotechnology Sensors, Systems, and Applications VIII (Vol. 9836, pp. 302-317). SPIE.;
  • Nothwang, W. D., Gremillion, G. M., Donavanik, D., Haynes, B. A., Atwater, C. S., Canady, J. D., … & Marathe, A. R. (2016, August). Multi-sensor fusion architecture for human-autonomy teaming. In 2016 Resilience Week (RWS) (pp. 166-171). IEEE.;
  • Koksalmis, E., Kabak, Ö. (2020, July) Sensor fusion based on Dempster-Shafer theory of evidence using a large scale group decision making approach, In International Journal of Intelligent Systems, 35(7) (pp. 1126-1162). https://doi.org/10.1002/int.22237

A244 PHase I

Algorithms for Modular Remote Expendable Sensor Array

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