Intelligence, Surveillance And Reconnaissance Systems Research Articles

Cyber Preparedness of the Indian Armed Forces

The Indian armed forces today face a significant threat of cyberattacks, particularly from hostile neighbouring states like China and Pakistan. Cyberattacks could potentially damage or cripple critical weapons platforms, major communications and ISR (intelligence, surveillance, and reconnaissance) systems, and vital infrastructure, undermining the military’s preparedness and war-fighting capabilities. How prepared is the Indian military to neutralise cyberattacks from adversaries such as China and Pakistan? This is the central question that this article attempts to address. The article contends that the delayed development of technical, policy, and organisational measures in the cyber domain indicates vulnerabilities in the Indian military’s current state of cyber preparedness, leaving room for enemy cyberattacks. It emphasises the need for immediate actions to address the critical gaps in India’s cybersecurity strategy. In particular, the Indian military must focus on drastically strengthening its defensive cyber capabilities to neutralise potential enemy cyberattacks on weapons platforms, communication and ISR systems, and critical infrastructure. The article concludes that without enhanced defensive cyber capabilities, India’s military modernisation may fall short of responding adequately to technology-driven warfare threats from the hostile states in the neighbourhood.

XUAVs: Towards Efficient Approximate Computing for UAVs—Low Power Approximate Adders With Single LUT Delay for FPGA-Based Aerial Imaging Optimization

High Definition (HD) image processing and real-time analytics over live video feeds have always been the key requirements for Intelligence, Surveillance and Reconnaissance (ISR) applications. With the evolution of optics and image enhancement techniques, computational loads of HD ISR systems are also rising exponentially. On the contrary, the slow-down of Moore's Law has recently posed challenging bounds over the level of achievable miniaturization for emerging processing and storage units. Field Programmable Gate Arrays (FPGAs) offer a popular choice of implementing ISR algorithms over resource-constrained platforms, such as Unmanned Aerial Vehicles (UAVs), due to favorable features of reconfigurability and rapid prototyping. A promising solution to bridge the gap between resource-constrained host platforms and computation-intensive FPGA applications is the paradigm of Approximate Computing. It compromises on the accuracy of processed results to offer significant performance gains for error-tolerant applications, such as video and image processing. In this paper, we present a novel approximate adder design methodology, for FPGA-based systems with improved SWaP performance, besides preserving the accuracy requirements within acceptable thresholds. The design methodology proposed in this paper focuses on the FPGA-specific Look-Up Table (LUT) architecture to introduce approximations while splitting the carry chain into LUT-based sub-adders, with flexible overlap to tune the adder's accuracy and achieve the overall latency of a single LUT. The paper presents several variants of the proposed design and offers application-oriented flexibility to adjust for optimal SWaP vs accuracy trade-off. We have further devised a comprehensive assessment approach to verify functional viability of the proposed atomic arithmetic blocks at system level, through their implementation into dense computational imaging applications, such as 2-dimensional Discrete Cosine Transform (DCT), airborne self-localization and moving object tracking algorithms, in comparison with other state-of-the-art adders. Our most accurate design performs at least 9.9% better in power consumption when compared with existing approximate adders, which proves that the proposed methodology holds promising potential to improve SWaP-index for computation-intensive UAV applications.

Optimal Human–Machine Teaming for a Sequential Inspection Operation

A novel mixed initiative optimal control system for intelligence, surveillance and reconnaissance (ISR) operations which entails human–machine teaming has been developed. The scenario entails a camera-equipped unmanned air vehicle sequentially overflying geolocated objects of interest, which need to be classified as either a true or false target by a human operator. The vehicle is allowed a prespecified number of revisits, such that an object can be looked at, a second time, under better viewing conditions. The overarching goal is to correctly classify the objects and minimize the false alarm (FA) and missed detection (MD) rates. We design a stochastic controller that computes if and when a revisit is necessary and also the optimal revisit state, i.e., viewing altitude and aspect angle. The concept of operation is such that the critical task of detection/pattern recognition is relegated to the human operator, whereas optimal decision making is entrusted to the machine. The stochastic dynamic programming-based decision algorithm is, however, informed about the performance of the human operator via an empirical human perception model. The model is experimentally obtained in the form of state-dependent confusion matrices. The optimal closed-loop ISR system is shown to experimentally achieve a FA rate of 5% and MD rate of 12%, which are significantly lower than the open-loop operator-only performance metrics. The performance improvements that were observed are relevant to a particular operator, and thus, the study suggests that the same improvements could conceivably be achieved with other test subjects.

Executable System of Systems Architecture Using OPM in Conjunction with Colored Petri Net: A Module for Flexible Intelligent and Learning Architectures for System of Systems

AbstractThe ability to coordinate intricate operations in both intelligence, surveillance and reconnaissance (ISR) as well as search and rescue (SAR) operations is a complex issue. Such operations can be described as a System of Systems (SoS). Both object process methodology (OPM) and colored petri nets (CPN) can be used to create executable architecture models for an SoS. The modeling approach proposed in this study can capture the interactions that occur between either collaborating systems or components. It can also be used to analyze the behavioral aspects of an SoS. OPM models are easy to understand, giving stakeholders the ability to envision architecture's quality and scale. In contrast, the simulation capabilities of CPN can be used for accurate performance assessment. The proposed modelling approach facilitates decision making by providing a better estimation and confidence of the SoS's performance. The proposed system development framework and its enabling technology is domain neutral. This model can be applied to a broad range of SoS at various levels of abstractions. A case study for an ISR system is presented as an example.