Bruce M. Sabol

Description of the IR sensor model for the countermine computational testbed

The goal of the Countermine Computational Testbed Sensor Model Development Program is to design a software simulation for candidate airborne imaging sensors suitable for use in the remote detection of mines. The simulation takes as input several sensor parameters and a time-dependent history of location and orientation of the sensor. A scene sampling module generates an array of query ray origins and directions from the view point through the image plane to obtain radiance values from other testbed modules. Blurring effects, including those from diffraction, aberrations, detector spacing, and digitization are accounted for by using results from the validated NVTherm model. In this way, the sensor systems modulation transfer function is imposed on the image. In addition, atmosphere effects are incorporated through the use of external scattering models. If necessary, the resulting radiance image is re-sampled at desired pixel locations. Finally, the detector response characteristics are applied to the radiance image for computation of signal voltages. A noise voltage is then added and the digitization process simulated to produce the final sensor output synthetic image. The model is implemented in C++ using object-oriented programming techniques that allow for flexible extension of the simulation to different types of sensors and geometries. Model design goals, techniques, components, and specific image synthesis algorithms implementations are discussed along with the presentation of example results.

Sensor-based validation of synthetic thermal scenes: how close is good enough?

Using synthetic background scenes in the modeling of thermal infrared sensor-based smart munitions offers tremendous flexibility in exploring the performance envelope of these systems. However, to reach this goal, the synthetic background generation process must undergo the scrutiny of verification and validation to be accredited for use with a specific sensor system. Traditional approaches to validating synthetic scenes range from low-level subjective comparison to absolute pixel-to-pixel agreement between the two scenes. Neither of these approaches considers the specific smart munition sensor and processor which ultimately use the scene. In this paper we present an alternate validation approach based on comparison between end performance of a thermal infrared sensor-based smart munition system using synthetic/real scene pairs. Paired synthetic/real thermal scenes, including a low and a high-clutter level, are compared with conventional validation metrics and with the performance-based metric, using various smart munition sensor targeting algorithms. The degree of scene fidelity (absolute agreement between scene pairs) required to replicate performance varies with clutter level and processor algorithm. Under high clutter conditions, greater synthetic scene fidelity is required to match performance.

Basic development and testing of a short-range laser profiling model

Laser profilometers hold the promise of improving smart munition detection and aimpoint selection performance when combined with data from other sensor types, such as passive thermal infrared. The high cost of physically testing sensor systems dictates that simulation should be used whenever possible. This paper describes the development and preliminary verification of a profilometer simulation developed as part of a larger smart munition sensor simulation model. A single-scattering laser profilometer model, which predicts returns from passive illumination sources, such as the sun and sky, in addition to laser returns, is formulated and implemented. Several simple scenarios are simulated to test model behavior as a function of environmental illumination, reflecting material, and target geometry. Results agree with expectations and show the importance including environmental conditions and detailed material reflective properties in the model.

Collection and evaluation of false alarm signatures in background data

A significant amount of background airborne data was collected as part of May 2005 tests for airborne minefield detection at an arid site. The locations of false alarms which occurred consistently during different runs, were identified and geo-referenced by MultiSensor Science LLC. Ground truth information, which included pictures, type qualifiers and some hyperspectral data for these identified false alarm locations, was surveyed by ERDC-WES. This collection of background data, and subsequent survey of the false alarm locations, is unique in that it is likely the first such airborne data collection with ground truthed and documented false alarm locations. A library of signatures for different sources of these false alarms was extracted in the form of image chips and organized into a self-contained database by Missouri SandT. The library contains target chips from airborne mid wave infrared (MWIR) and multispectral imaging (MSI) sensors, representing data for different days, different times of day and different altitudes. Target chips for different surface mines were also added to the database. This database of the target signatures is expected to facilitate evaluation of spectral and shape characteristics of the false alarms, to achieve better false alarm mitigation and improve mine and minefield detection for airborne applications. The aim of this paper is to review and summarize the data collection procedure used, present the currently available database of target chips and make some recommendations regarding future data collections.

Methodology of real-time simulation to support field testing of dual-mode smart munitions sensors

This paper describes the methodology for executing real-time simulations for the support of field testing of smart munition sensors. The sensor simulated is a dual-mode sensor using passive thermal infrared and active laser profiling. The types of tests supported by the simulation are dynamic flight tests over stationary targets, captive flight tests with moving tactical targets, and end-to-end system tests with dynamic flight over moving tactical targets. The components of this methodology that will be discussed include the sensor simulation model, target and background models, and measurement procedures for generating inputs required for target and background models. The resulting simulation capability can be used to support a wide range of evaluations including concept evaluation, subsystem design trade-off analysis, and system performance evaluation.

Smart munition thermal sensor model for evaluating effects of terrain and environment

Current engineering-level smart munition sensor models emphasize sensor/target interactions with detection and aim-point information being the principal outputs. Background is not treated with the same fidelity. False alarm rate is based on captive flight statistics and is not actually simulated. The lack of a means to evaluate effects of background in an end-to-end simulation mode motivated the development of the WES Smart Munition Thermal Sensor Model. The model consists of a generic set of algorithms used to simulate platform dynamics, scanning geometry, and infrared sensor optics and electronics. Thermal target models of a vehicle developed by Georgia Tech Research Institute are instantiated into a background scene consisting of calibrated thermal imagery. Parameters are set to reflect the flight dynamics, scanning, optics and electronics of a specified munition, and the output voltage is processed though an appended target acquisition algorithm. A hypothetical smart munition with a thermal sensor (simple flying spot detector) is configured and flown over high-resolution thermal imagery obtained from selected locations to demonstrate effects of varied terrain and environmental conditions.