Infrared Search and Track systems are an essential element of the modern and future combat aircrafts. Passive automatic
search, detection and tracking functions, are key points for silent operations or jammed tactical scenarios.
SKYWARD represents the latest evolution of IRST technology in which high quality electro-optical components,
advanced algorithms, efficient hardware and software solutions are harmonically integrated to provide high-end
affordable performances. Additionally, the reduction of critical opto-mechanical elements optimises weight and volume
and increases the overall reliability.
Multiple operative modes dedicated to different situations are available; many options can be selected among multiple or
single target tracking, for surveillance or engagement, and imaging, for landing or navigation aid, assuring the maximum
The high quality 2D-IR sensor is exploited by multiple parallel processing chains, based on linear and non-linear
techniques, to extract the possible targets from background, in different conditions, with false alarm rate control.
A widely tested track processor manages a large amount of candidate targets simultaneously and allows discriminating
real targets from noise whilst operating with low target to background contrasts.
The capability of providing reliable passive range estimation is an additional qualifying element of the system.
Particular care has been dedicated to the detector non-uniformities, a possible limiting factor for distant targets detection,
as well as to the design of the electro-optics for a harsh airborne environment.
The system can be configured for LWIR or MWIR waveband according to the customer operational requirements. An
embedded data recorder saves all the necessary images and data for mission debriefing, particularly useful during inflight
system integration and tuning.
This paper presents a soft-real time simulator for IRST (InfraRed Search and Track) systems with ATR (Automatic
Target Recognition) embedded functions to test airborne applications performance. The IR camera model includes
detector, optics, available Field-of-Regard, etc., and it is integrated with the motion platform local stabilization system
to consider all factors impacting IR images. The atmosphere contributions are taken into account by means of a link to
ModTran computer program. Sensor simulation allows derivation and assessment of IR Figures of Merit (NEI, NETD,
SNR...). IR signatures of targets derive both from data collected in specific trial campaigns and from laboratory built
models. The simulation of the scan procedure takes into account different policies (ground points paths or defined
angular volume) and different platform motion strategies (continuous or step steering scan). The scan process includes
Kalman technique to face unexpected variations of aircraft motion. Track and ATR processors are simulated and run
consistently on the output of the sensor model. The simulator functions are developed in MatLab and SIMULINK and
then exported in C code to be integrated in soft real-time environment.
The use of this simulator supports the definition and design of the IRST systems especially for the evaluation of the
most demanding operative requirements. An application of this simulator is for the NEURON UCAV (Unmanned
Combat Air Vehicle) technological demonstrator, which accommodates on board both IRST and ATR tasks.
Template-matching techniques for automatic detection of multiple, extended, and low contrast targets in infrared maritime scenarios are described and analyzed. In particular, we focus our attention on the specific area of the sea around the horizon, where common techniques of clutter removal, based on target contrast only, fail. Targets of interest are ships along the horizon line in adverse atmosphere conditions, with dim contrast with respect to the background. A database of ship images is used for the analysis. We conclude that the normalized cross-correlation (NCC) technique is a reasonable choice for this application due to its capability to provide an estimate of the similarity between images, even if they present different energy levels and are corrupted by noise. It also is more tolerant to the geometric distortions. After a description of the test setup, simulation results are presented to show the performances of the proposed technique; examples using both synthetic and real images are considered.
Proc. SPIE. 5573, Image and Signal Processing for Remote Sensing X
KEYWORDS: Target detection, Infrared search and track, Long wavelength infrared, Infrared imaging, Detection and tracking algorithms, Sensors, Personal digital assistants, Infrared radiation, Motion models, Data fusion
This paper describes an application of the IMM (Interacting Multiple Model) technique in a multiple target tracking system for an IRST (Infrared Search and Track) system operating in the mid and in the long wave infrared bands. The use of the two IR bands allows better performances in terms of detection probability, lower false tracks and short time for track initiation. To properly merge data from the two sensors, an enhancement of the PDA (Probabilistic Data Association) technique is introduced in the process. The approach has shown to properly operate with a very high number of possible targets in the two IR bands. Good results have been obtained also in the case of clustered detections, as well as in uniformly space distributed detections.
Test results of Automatic Target Recognition of IR images are described. They are mainly based on Template Matching Techniques and Synthetic Discriminant Functions (SDF) filters are adopted to increase the robustness of the method and to reduce computational load. Extensive tests are performed with a number of different scenarios and image noise levels. Dedicated refinements and operative adjustements to traditional approaches are implemented and described. The work has been originated with digitally generated target databases and proceeds with real IR images.