Gillian Fiona Marshall

High-dynamic-range front end for automatic image processing applications

A camera system is described which can be used as a front end to automatic image processing applications, capable of imaging very high dynamic range scenes. We were inspired by the mammalian retina to build a system entirely upon commercially-available components which has the maximum flexibility and minimum risk and development cost. The result is a system which includes logarithmic photodetectors, together with simple robust fixed pattern noise correction and high-pass spatial filtering.

Toward sensor modular autonomy for persistent land intelligence surveillance and reconnaissance (ISR)

Currently, most land Intelligence, Surveillance and Reconnaissance (ISR) assets (e.g. EO/IR cameras) are simply data collectors. Understanding, decision making and sensor control are performed by the human operators, involving high cognitive load. Any automation in the system has traditionally involved bespoke design of centralised systems that are highly specific for the assets/targets/environment under consideration, resulting in complex, non-flexible systems that exhibit poor interoperability. We address a concept of Autonomous Sensor Modules (ASMs) for land ISR, where these modules have the ability to make low-level decisions on their own in order to fulfil a higher-level objective, and plug in, with the minimum of preconfiguration, to a High Level Decision Making Module (HLDMM) through a middleware integration layer. The dual requisites of autonomy and interoperability create challenges around information fusion and asset management in an autonomous hierarchical system, which are addressed in this work. This paper presents the results of a demonstration system, known as Sensing for Asset Protection with Integrated Electronic Networked Technology (SAPIENT), which was shown in realistic base protection scenarios with live sensors and targets. The SAPIENT system performed sensor cueing, intelligent fusion, sensor tasking, target hand-off and compensation for compromised sensors, without human control, and enabled rapid integration of ISR assets at the time of system deployment, rather than at design-time. Potential benefits include rapid interoperability for coalition operations, situation understanding with low operator cognitive burden and autonomous sensor management in heterogenous sensor systems.

An architecture for sensor modular autonomy for counter-UAS

This paper discusses a modular system architecture for detection, classification and localisation of Unmanned Aerial System (UAS) targets, consisting of intelligent Autonomous Sensor Modules (ASMs), a High-Level Decision Making Module (HLDMM), a middleware integration layer and an end-user GUI, under the previously-reported SAPIENT framework. This enables plug and play sensor integration and autonomous fusion, including prediction of the trajectory of the vehicle for sensor cueing, multi-modal sensor fusion and target hand-off. The SAPIENT Counter-UAS (C-UAS) system was successfully demonstrated in a live trial against a range of UAS targets flown in a variety of attack trajectories, using radar and Electro-Optic (EO) C-UAS ASMs. In addition, the trial also demonstrated the use of synthetic sensors, on their own and in combination with real sensors. Outputs of all the available sensors were tracked and fused by the Cubica SAPIENT HLDMM which then steered narrow field-of-view cameras onto the predicted 3D position of the UAS. The operator was provided with a map-based view showing alerts and tracks to provide situational awareness, together with snapshots from the EO sensor and video feeds from the steerable narrow field of view cameras. This demonstrates an effective C-UAS system operating entirely autonomously, with autonomous detection, localisation, classification, tracking, fusion and sensor management, leading to “eyes on” the aerial threat, and all happening with zero operator intervention and in real-time.

An autonomous sensor module based on a legacy CCTV camera

A UK MoD funded programme into autonomous sensors arrays (SAPIENT) has been developing new, highly capable sensor modules together with a scalable modular architecture for control and communication. As part of this system there is a desire to also utilise existing legacy sensors. The paper reports upon the development of a SAPIENT-compliant sensor module using a legacy Close-Circuit Television (CCTV) pan-tilt-zoom (PTZ) camera. The PTZ camera sensor provides three modes of operation. In the first mode, the camera is automatically slewed to acquire imagery of a specified scene area, e.g. to provide “eyes-on” confirmation for a human operator or for forensic purposes. In the second mode, the camera is directed to monitor an area of interest, with zoom level automatically optimized for human detection at the appropriate range. Open source algorithms (using OpenCV) are used to automatically detect pedestrians; their real world positions are estimated and communicated back to the SAPIENT central fusion system. In the third mode of operation a “follow” mode is implemented where the camera maintains the detected person within the camera field-of-view without requiring an end-user to directly control the camera with a joystick.