Richard B. Sanderson

Correlated two-color midinfrared background characteristics

Multicolor discrimination techniques provide a useful approach to suppressing background clutter and reducing false alarm rates in warning sensors. To assess discrimination performance, it is necessary to understand the statistics of each band as well as inter-band correlations. This paper describes the background measurements from an airborne platform collected using a two-color prototype staring missile- warning sensor. The sensor is a commercial 256x256 InSb camera with filter wheel integrated into a 90 deg by 90 deg. optic. The two colors lie in the carbon dioxide red spike region and in the window region below 4 micrometers. These bands are useful for detecting the combustion of hydrocarbons in the presence of background clutter. The sensor looks straight down from the aircraft and data is collected at frame rates from 10 to 100 Hz. Extensive background data has been collected over a wide range of scenes representing industrial, urban, rural, mountainous, and shoreline terrain. The data has been analyzed to provide correlated statistics of these spectral bands for both the underlying background structure and discrete false alarm sources. This data provides a basis for estimating the performance of spectral discrimination and optimizing processing algorithms for the suppression of clutter and rejection of false alarms.

Tactical mid-infrared background suppression in heavy clutter environments

Effective missile warning and countermeasures are 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 needed. The largest concern is 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 generators like burning fuels, flares, exploding ordinance, and industrial sources. 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 fielded scanning sensors. Utilization of the background and clutter spectral content, coupled with additional spatial and temporal filtering techniques have resulted in a robust real-time algorithm to increase signal-to-clutter ratios against point targets. Algorithm results against tactical data are summarized and compared in terms of computational cost as implemented on a real-time 1024 SIMD machine.

An advanced missile warning processing suite

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.

Near infrared missile warning testbed sensor

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.

A spectral independent morphological adaptive classifier

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.

Tactical midinfrared testbed

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 fielded scanning sensors. Normally, multicolor discrimination is performed in the mid-infrared, 3-5 micrometers 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 staring mosaic detector arrays that provide much higher frame rates than scanning systems in a more compact and mechanically simpler package. This, in turn, has required that multicolor clutter data be collected for both analysis and algorithm development. The developed sensor test bed is a 256x256 InSb sensor with an optimized two color filter wheel integrated with the optics. The collection portion includes a ruggedized parallel array processor and fast disk array capable of real-time processing and collection of up to 350 full frames per second. This configuration allowed the collection and real- time processing of temporally correlated, radiometrically calibrated data in two spectral bands that was compared to background and target imagery taken previously. The current data collections were taken from a modified Piper light aircraft at medium and low altitudes of background, battlefield clutter, and shoulder-fired missile signatures during August 1999.

Performance of SIMAC algorithm suite for tactical missile warning

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 .

Empirical modeling and results of NIR clutter for tactical missile warning

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.

Empirical performance of the spectral independent morphological adaptive classifier

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 todays 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.

Clutter and signatures from near infrared testbed sensor

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.