- In June 2018, Nathan J. Gulosh authored a thesis on drone swarm sensors titled, “Employment of Intelligence, Surveillance, and Reconnaissance Drone Swarms to Enhance Ground Combat Operations.”
- Michael D. Baird authored a dissertation on the potential gaps in applying AI integrations to ISR operations titled, “Implications of Artificial Intelligence Integration Into Intelligence Surveillance and Reconnaissance Operations.”
- In a thesis by Donald Glen Church, he sought to ease the processing of time-critical information and decision-making by developing a system that considers the personality of the decision-maker. The report is titled “Reducing Error Rates in Intelligence, Surveillance, and Reconnaissance (ISR) Anomaly Detection via Information Presentation Optimization.”
This report contains a list of eight (8) dissertations and master's theses that discuss the advancement of Intelligence, Surveillance, and Reconnaissance (ISR), including their authors, contact information, pricing, and awarding institutions. The required information has been entered in the attached spreadsheet.
Employment of Intelligence, Surveillance, and Reconnaissance Drone Swarms to Enhance Ground Combat Operations
- This thesis was authored by Nathan J. Gulosh at the Naval Postgraduate School in June 2018.
- The report aims to determine drone swarm sensor requirements and employment tactics, techniques, and procedures. It explored drone swarm employment in support of a Marine infantry company, compiling data from 30,000 simulated missions.
- According to the findings of this research, simultaneous drone swarm employment allows teams to “target and engage twice as many enemy combatants” compared to the individual drone employment currently used in ISR. It also found that larger drone swarms have fewer sensor requirements.
- This report focuses on new or emerging classes of sensor technology and potential applications of robotic process automation (RPA) technologies to ISR.
Implications of Artificial Intelligence Integration Into Intelligence Surveillance and Reconnaissance Operations
- This dissertation was authored by Michael D. Baird, Lt Col, USAF at Air University in 2020.
- The report aims to investigate potential gaps in applying AI integrations to ISR operations. It examined the driving forces and technologies of AI and identified the advantages and risks of such integrations.
- According to the findings of this report, the potential gaps are:
- Building trust and an understanding of AI integrations and their uses.
- Difficulty in training ISR personnel to use artificial intelligence (AI) and machine learning (ML) technologies.
- The occurrence of unique operational implications — which must be promptly addressed.
- This report explores the potential applications of artificial intelligence (AI), machine learning (ML), and robotic process automation (RPA) technologies to ISR.
Multilateration and Kalman Filtering Techniques for Stealth Intelligence Surveillance and Reconnaissance Using Multistatic Radar
- This thesis was authored by Abdulhak Nagy at Carleton University in 2016.
- The report aims to address the lag in attempting to “hybrid-geolocate and track a moving stealth target with only two receivers,” making it impossible to monitor borders between countries and identify incoming target stealth threats.
- Based on the findings of this report, it is possible to hybrid-geolocate and track using the dual-stage method. This way, one can reduce the “number of receivers required to two while keeping track of target geolocations.”
- This report offers new techniques for identifying relationships among collected data.
Reducing Error Rates in Intelligence, Surveillance, and Reconnaissance (ISR) Anomaly Detection via Information Presentation Optimization
- This thesis was authored by Donald Glen Church at Wright State University in 2015.
- The report aims to ease the process of time-critical decision-making by taking into account the personality type of the decision-maker. It also developed a model to demonstrate how decision-makers absorb information and use them in making identifications.
- As discovered in this report, the new model can help the ISR community in “developing an adaptive aiding system to reduce the cycle time in the decision-making process and have the greatest impact on performance.”
A Framework for Developing Executable Architecture for Aerial Intelligence Surveillance and Reconnaissance Systems-of-Systems: A Systems Dynamics Approach
- This thesis was authored by Steven Chetcuti at the Georgia Institute of Technology in 2020.
- The report developed and tested a framework for the development of a holistic executable architecture for complex systems-of-systems. The framework was tested using the Aerial Intelligence, Surveillance, and Reconnaissance (AISR) system-of-systems architecture.
- The results of the experiments supported the use of “System Dynamics as a means to holistically assess complex systems-of-systems in a rapidly developed, interactive environment that enables trades.”
Determining Intelligence, Surveillance, and Reconnaissance (ISR) System Effectiveness, and Integration as part of Force Protection and System Survivability
- This thesis was authored by Sze Shiang Soh at the Naval Postgraduate School in 2013.
- The report considers the concept of situation awareness by analyzing the impact of the ISR system's “effectiveness and integration on unit survivability” within the context of a combined arms unit.
- According to the findings of the report, enhanced sensors and a properly integrated ISR system can positively impact the “overall battle space awareness, leading to overall unit survivability.” However, the integration of different arrays of sensor systems may be a daunting task in the absence of appropriate policies.
In answering this request, the research team combed the databases of numerous postgraduate schools and colleges for dissertations and theses relating to ISR. Then, we thoroughly examined each of the selected reports to match the given criteria.
For the contact information of the authors, we relied on LinkedIn. However, we could not provide any contact information for Nathan J. Gulosh and Sze Shiang Soh. We searched various social media profiles on LinkedIn, Facebook, and Instagram but could not find any account to match both authors. We also could not retrieve their email addresses on websites such as Hunter.io and Rocket Reach, as there was no available information attached to both names. Additional information has been provided in the attached spreadsheet.