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
system flexibility.
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.
KEYWORDS: Sensors, Data fusion, Radar, Infrared search and track, Process modeling, Motion models, Error analysis, Data processing, Detection and tracking algorithms, Sensor fusion
An algorithm is proposed here that overcomes the problem of the not exhaustive number of common measures from sensors
of different kind. In the presence of a suite of heterogeneous sensors, the data fusion process has to deal with the problem
of managing different information, generally not directly comparable. The analysis of the mathematical model is carried
out considering a data fusion system between radar and Infrared Search and Track (IRST) where the measurement of the
range is achieved by radar only. Simulation results demonstrate the effectiveness of the algorithm as regards the fusion
process, tracking and correctness of association among tracks from different sensors. A comparison with a known approach
from the literature about the fusion equation is also performed.
KEYWORDS: Radar, Infrared search and track, Data fusion, Error analysis, Process modeling, Motion models, Sensors, Data processing, Optical engineering, Detection and tracking algorithms
An algorithm for the fusion of data used for target search and tracking originated by a bidimensional radar and infrared search and track is proposed and described. To permit a fusion process between two systems whose measurements are not completely comparable, a new strategy for mixing the system states is introduced, thus obtaining a set of homogeneous measurements. We describe the rationale behind the method and develop mathematical aspects necessary to obtain the equation for the fusion of tracking data. An analysis of the estimation errors associated with the proposed model is also described. Some simulation results demonstrate the capabilities of the presented technique.
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.
KEYWORDS: Sensors, Target detection, Infrared search and track, Mid-IR, Long wavelength infrared, Transmittance, Data acquisition, Sun, Data fusion, Signal to noise ratio
Modern naval warfare asks for alerting system able to detect classical & asymmetric threats in support to radar in
environment in which radar has reduced performance (on or near the sea surface). More, capability to offer high
resolution images helps in ship identification, coastal & harbor surveillance as well as night navigation and rescue.
All these tasks can be performed by an infrared search and track (IRST) system.
This paper describes the IRST named Silent Acquisition and Surveillance System (SASS), developed for the Italian
Navy and the tests jointly carried out by SELEX GALILEO and Italian Navy to characterize the system.
KEYWORDS: Sensors, Target detection, Personal digital assistants, Data fusion, Detection and tracking algorithms, Motion models, Optical engineering, Infrared search and track, Long wavelength infrared, Infrared sensors
We describe a new approach to fusion techniques in a multiple target tracking system for an infrared search and track (IRST) system operating in the mid- and long-wave infrared (IR) bands. The use of the two IR bands allows better performances in terms of detection probability, lower number of false tracks, and shorter time for track declaration. To properly merge data from the two sensors, an enhancement of the probabilistic data association (PDA) technique is introduced in the process. A simplification of the approach used brings back the algorithm to the well-known PDA, allowing its use in conjunction with the interacting multiple model (IMM) with increased tracking ability of the system.
KEYWORDS: Personal digital assistants, Target detection, Sensors, Motion models, Infrared search and track, Detection and tracking algorithms, Infrared radiation, Data fusion, Infrared imaging, Long wavelength infrared
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.
The paper describes a fast and accurate algorithm of IR background noise and clutter generation for application in scene simulations. The process is based on the hypothesis that background might be modeled as a statistical process where amplitude of signal obeys to the Gaussian distribution rule and zones of the same scene meet a correlation function with exponential form. The algorithm allows to provide an accurate mathematical approximation of the model and also an excellent fidelity with reality, that appears from a comparison with images from IR sensors. The proposed method shows advantages with respect to methods based on the filtering of white noise in time or frequency domain as it requires a limited number of computation and, furthermore, it is more accurate than the quasi random processes. The background generation starts from a reticule of few points and by means of growing rules the process is extended to the whole scene of required dimension and resolution. The statistical property of the model are properly maintained in the simulation process. The paper gives specific attention to the mathematical aspects of the algorithm and provides a number of simulations and comparisons with real scenes.
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