Self protection of airborne assets has been important to the Air Force and DoD community for many years. The greatest threats to aircraft continue to be man portable air defense missiles and ground fire. AFRL has been pursuing a near-IR sensor approach that has shown to have better performance than midwave IR systems with much lower costs. SIMAC couples multiple spatial and temporal filtering techniques to provide the needed clutter suppression in the NIR missile warning systems. Results from flight tests will be discussed .
A tactical airborne multicolor missile warning testbed was developed as part of an Air Force Research Laboratory (AFRL) initiative focusing on the development of sensors operating in the near infrared where commercially available silicon detectors can be used. The presentation will detail the new background and clutter data collections from ground and flight operations and results. It will outline the statistical analysis in both detection and guard bands to provide a basis for evaluation of sensor performance against missile and hostile fire threats. A general stochastic model for the NIR clutter will be presented and validity compared against flight data.
The effectiveness of autonomous munitions systems can be enhanced by transmitting target images to a man-in-the-loop
(MITL) as the system deploys. Based on the transmitted images, the MITL could change target priorities or conduct
damage assessment in real-time. One impediment to this enhancement realization is the limited bandwidth of the system
data-link. In this paper, an innovative pattern-based image compression technology is presented for enabling efficient
image transmission over the ultra-low bandwidth system data link, while preserving sufficient details in the
decompressed images for the MITL to perform the required assessments. Based on a pattern-driven image model, our
technology exploits the structural discontinuities in the image by extracting and prioritizing edge segments with their
geometric and intensity profiles. Contingent on the bit budget, only the most salient segments are encoded and
transmitted, therefore achieving scalable bit-streams. Simulation results corroborate the technology efficiency and
establish its subjective quality superiority over JPEG/JPEG2000 as well as feasibility for real-time implementation.
Successful technology demonstrations were conducted using images from surrogate seekers in an aircraft and from a
captive-carry test-bed system. The developed technology has potential applications in a broad range of network-enabled
weapon systems.
Effective missile warning and countermeasures continue to be an unfulfilled goal for the Air Force including the wider military and civilian aerospace community. To make the necessary detection and jamming timeframes dictated by today's proliferated missiles and near-term upgraded threats, sensors with required sensitivity, field of regard, and spatial resolution are being pursued in conjunction with advanced processing techniques allowing for detection and discrimination beyond 10 km. The greatest driver of any missile warning system is detection and correct declaration, in which all targets need to be detected with a high confidence and with very few false alarms. Generally, imaging sensors are limited in their detection capability by the presence of heavy background clutter, sun glints, and inherent sensor noise. Many threat environments include false alarm sources like burning fuels, flares, exploding ordinance, and industrial emitters. Spectral discrimination has been shown to be one of the most effective methods of improving the performance of typical missile warning sensors, particularly for heavy clutter situations. Its utility has been demonstrated in the field and on-board multiple aircraft. Utilization of the background and clutter spectral content, coupled with additional spatial and temporal filtering techniques, have yielded robust adaptive real-time algorithms to increase signal-to-clutter ratios against point targets, and thereby to increase detection range. The algorithm outlined is the result of continued work with reported results against visible missile tactical data. The results are summarized and compared in terms of computational cost expected to be implemented on a real-time field-programmable gate array (FPGA) processor.
A new tactical airborne multicolor missile warning testbed was developed as part of an Air Force Research Laboratory (AFRL) initiative focusing on the development of sensors operating in the near infrared where commercially available silicon detectors can be used. At these wavelengths, the rejection of solar induced false alarms is a critical issue. Multicolor discrimination provides one of the most promising techniques for improving the performance of missile warning sensors, particularly for heavy clutter situations. This, in turn, requires that multicolor clutter data be collected for both analysis and algorithm development.
The developed sensor test bed, as described in previous papers1, is a two-camera system with 1004x1004 FPA coupled with optimized filters integrated with the optics. The collection portion includes a high speed processor coupled with a high capacity disk array capable of collecting up to 48 full frames per second. This configuration allows the collection of temporally correlated, radiometrically calibrated data in two spectral bands that provide a basis for evaluating the performance of spectral discrimination algorithms.
The presentation will describe background and clutter data collected from ground and flight locations in both detection and guard bands and the statistical analysis to provide a basis for evaluation of sensor performance. In addition, measurements have been made of discrete targets, both threats and false alarms. The results of these measurements have shown the capability of these sensors to provide a useful discrimination capability to distinguish threats from false alarms.
Multicolor discrimination is one of the most effective ways of improving the performance of infrared missile
warning sensors, particularly for heavy clutter situations. A new tactical airborne multicolor missile warning testbed
was developed and fielded as part of a continuing Air Force Research Laboratory (AFRL) initiative focusing on clutter
and missile signature measurements for effective missile warning algorithms. The developed sensor test bed is a multi-camera
system 1004x1004 FPA coupled with optimized spectral filters integrated with the optics; a reduced form factor
microprocessor-based video data recording system operating at 48 Hz; and a real time field programmable gate array
processor for algorithm and video data processing capable of 800B Multiply/Accumulates operations per second. A
detailed radiometric calibration procedure was developed to overcome severe photon-limited operating conditions due
to the sub-nanometer bandwidth of the spectral filters. This configuration allows the collection and real-time processing
of temporally correlated, radiometrically calibrated video data in multiple spectral bands. The testbed was utilized to
collect false alarm sources spectra and Man-Portable Air Defense System (MANPADS) signatures under a variety of
atmospheric and solar illuminating conditions. Signatures of approximately 100 missiles have been recorded.
Effective missile warning and countermeasures remain an unfulfilled goal for the Air Force and others in the DOD community. To make the expectations a reality, newer sensors exhibiting the required sensitivity, field of regard, and spatial resolution are being developed and transitioned. The largest concern is in the first stage of a missile warning system: detection, in which all targets need to be detected with a high confidence and with very few false alarms. Typical fielded sensors are limited in their detection capability by either lack of sensitivity or by the presence of heavy background clutter, sun glints, and inherent sensor noise. Many threat environments include false alarm sources like burning fuels, flares, exploding ordinance, arc welders, and industrial emitters. Multicolor discrimination has been shown as one of the effective ways to improve the performance of missile warning sensors, particularly for heavy clutter situations. Its utility has been demonstrated in multiple demonstration and fielded systems. New exploitations of background and clutter spectral contents, coupled with advanced spatial and temporal filtering techniques, have resulted in a need to have a new baseline algorithm on which future processing advances may be judged against. This paper describes the AFRL Suite IIIc algorithm chain and its performance against long-range dim targets in clutter.
The Sensors Directorate of the Air Force Research Laboratory (AFRL), in conjunction with the Global Hawk
Systems Group, the J-UCAS System Program Office and contractor Defense Research Associates, Inc. (DRA) is
conducting an Advanced Technology Demonstration (ATD) of a sense-and-avoid capability with the potential to
satisfy the Federal Aviation Administration's (FAA) requirement for Unmanned Aircraft Systems (UAS) to
provide "an equivalent level of safety, comparable to see-and-avoid requirements for manned aircraft". This FAA
requirement must be satisfied for UAS operations within the national airspace. The Sense-and-Avoid, Phase I
(Man-in-the-Loop) and Phase II (Autonomous Maneuver) ATD demonstrated an on-board, wide field of regard,
multi-sensor visible imaging system operating in real time and capable of passively detecting approaching
aircraft, declaring potential collision threats in a timely manner and alerting the human pilot located in the
remote ground control station or autonomously maneuvered the aircraft. Intruder declaration data was collected
during the SAA I & II Advanced Technology Demonstration flights conducted during December 2006. A total of
27 collision scenario flights were conducted and analyzed. The average detection range was 6.3 NM and the mean
declaration range was 4.3 NM. The number of false alarms per engagement has been reduced to approximately 3
per engagement.
Effective missile warning and countermeasures continue to be an unfulfilled goal for the Air Force and DOD
community. To make the expectations a reality, sensors exhibiting the required sensitivity, field of regard, and spatial
resolution are being pursued. The largest concern is in the first stage of a missile warning system, detection, in which all
targets need to be detected with a high confidence and with very few false alarms. Typical sensors are limited in their
detection capability by the presence of heavy background clutter, sun glints, and inherent sensor noise. Many threat
environments include false alarm sources like burning fuels, flares, exploding ordinance, and industrial emitters.
Multicolor discrimination is one of the effective ways of improving the performance of missile warning sensors,
particularly for heavy clutter situations. Its utility has been demonstrated in multiple fielded systems. Utilization of the
background and clutter spectral content, coupled with additional spatial and temporal filtering techniques, have resulted
in a robust adaptive real-time algorithm to increase signal-to-clutter ratios against point targets. The algorithm is
outlined and results against tactical data are summarized and compared in terms of computational cost expected to be
implemented on a real-time field-programmable gate array (FPGA) processor.
A new tactical airborne multicolor missile warning testbed was developed and fielded as part of an Air Force Research Laboratory (AFRL) initiative focusing on clutter and missile signature measurements for algorithm development. Multicolor discrimination is one of the most effective ways of improving the performance of infrared missile warning sensors, particularly for heavy clutter situations. Its utility has been demonstrated in multiple fielded sensors. Traditionally, multicolor discrimination has been performed in the mid-infrared, 3-5 μm band, where the molecular emission of CO and CO2 characteristic of a combustion process is readily distinguished from the continuum of a black body radiator. Current infrared warning sensor development is focused on near infrared (NIR) staring mosaic detector arrays that provide similar spectral discrimination in different bands to provide a cost effective and mechanically simpler system. This, in turn, has required that multicolor clutter data be collected for both analysis and algorithm development.
The developed sensor test bed is a multi-camera system 1004x1004 FPA coupled with optimized filters integrated with the optics. The collection portion includes a ruggedized field-programmable gate array processor coupled with with an integrated controller/tracker and fast disk array capable of real-time processing and collection of up to 60 full frames per second. This configuration allowed the collection and real-time processing of temporally correlated, radiometrically calibrated data in multiple spectral bands that was then compared to background and target imagery taken previously
The Sensors Directorate at the Air Force Research Laboratory (AFRL) along with Defense Research Associates, Inc. (DRA) conducted a flight demonstration of technology that could potentially satisfy the Federal Aviation Administration's (FAA) requirement for Unmanned Aerial Vehicles (UAVs) to sense and avoid local air traffic sufficient to provide an "...equivalent level of safety, comparable to see-and-avoid requirements for manned aircraft". This FAA requirement must be satisfied for autonomous UAV operation within the national airspace. The real-time on-board system passively detects approaching aircraft, both cooperative and non-cooperative, using imaging sensors operating in the visible/near infrared band and a passive moving target indicator algorithm. Detection range requirements for RQ-4 and MQ-9 UAVs were determined based on analysis of flight geometries, avoidance maneuver timelines, system latencies and human pilot performance. Flight data and UAV operating parameters were provided by the system program offices, prime contractors, and flight-test personnel. Flight demonstrations were conducted using a surrogate UAV (Aero Commander) and an intruder aircraft (Beech Bonanza). The system demonstrated target detection ranges out to 3 nautical miles in nose-to-nose scenarios and marginal visual meteorological conditions. (VMC) This paper will describe the sense and avoid requirements definition process and the system concept (sensors, algorithms, processor, and flight rest results) that has demonstrated the potential to satisfy the FAA sense and avoid requirements.
The Passive Ground Moving Target Indication (PGMTI) program was a demonstration of the feasibility of autonomously detecting and tracking moving targets on the ground in real-time. Several scenarios were coordinated with ground vehicles and personnel so that the performance capabilities as well as any limitations of the system could be demonstrated. The objectives of the program were to demonstrate the following for the PGMTI system:
1.Process data in real time
*PMTD Algorithm
*High Pass Spatial Filter
*Tracker, Declaration Logic and Data Logger
2.Meet performance metrics
*Probability of Detection; PD >= 80%
*Probability of False Detection; PFD <= 0.02%
*Probability of Track; PT >= 90%
*Probability of False Track; PFT <= 0.01%
DRA used two different sensors to demonstrate the PGMTI performance; a near-IR and a mid-IR sensor. The near-IR system operated in real-time, and the mid-IR system was non-real-time. The approach to demonstrate the performance of the PGMTI system was to perform the following two tasks for each planned scenario containing the ground moving targets:
1)Collect the real-time processed data, from the near-IR sensor, which consists of the sensor data and a track file containing data on all tracks established by the system, and to analyze the results on the ground.
2)Collect raw data from the mid-IR sensor, post-process this data, and analyze results to determine performance. The analysis showed that all objectives were met provided there was at least 5% contrast between the ground moving target and its background. The PGMTI system concept demonstrated excellent performance and continued development is warranted.
Missile warning is one of the most significant problems facing aircraft flying into regions of unrest around the world. Recent advances in technology provide new avenues for detecting these threats and have permitted the use of imaging detectors and multi-color systems. Detecting threats while maintaining a low false alarm rate is the most demanding challenge facing these systems. Using data from ARFL's Spectral Infrared Detection System (SIRDS) test bed, the efficacy of alternative spectral threat detection algorithms developed around these technologies are evaluated and compared. The data used to evaluate the algorithms cover a range of clutter conditions including urban, industrial, maritime and rural. Background image data were corrected for non-uniformity and filtered to enhance threat to clutter response. The corrected data were further processed and analyzed statistically to determine probability of detection thresholds and the corresponding probability of false alarm. The results are summarized for three algorithms including simple threshold detection, background normalized analysis, and an inter-band correlation detection algorithm.
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