CERES :: Browsing by Author "Richardson, Mark A."

Browsing by Author "Richardson, Mark A."

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Open Access
3D automatic target recognition for future LIDAR missiles
(IEEE, 2017-01-10) Kechagias-Stamatis, Odysseas; Aouf, Nabil; Richardson, Mark A.
We present a real-time three-dimensional automatic target recognition approach appropriate for future light detection and ranging-based missiles. Our technique extends the speeded-up robust features method into the third dimension by solving multiple two-dimensional problems and performs template matching based on the extreme case of a single pose per target. Evaluation on military targets shows higher recognition rates under various transformations and perturbations at lower processing time compared to state-of-the-art approaches.
Open Access
Benchmarking of local feature detectors and descriptors for multispectral relative navigation in space
(Elsevier, 2020-04-07) Rondao, Duarte; Aouf, Nabil; Richardson, Mark A.; Dubois-Matra, Olivier
Optical-based navigation for space is a field growing in popularity due to the appeal of efficient techniques such as Visual Simultaneous Localisation and Mapping (VSLAM), which rely on automatic feature tracking with low-cost hardware. However, low-level image processing algorithms have traditionally been measured and tested for ground-based exploration scenarios. This paper aims to fill the gap in the literature by analysing state-of-the-art local feature detectors and descriptors with a taylor-made synthetic dataset emulating a Non-Cooperative Rendezvous (NCRV) with a complex spacecraft, featuring variations in illumination, rotation, and scale. Furthermore, the performance of the algorithms on the Long Wavelength Infrared (LWIR) is investigated as a possible solution to the challenges inherent to on-orbit imaging in the visible, such as diffuse light scattering and eclipse conditions. The Harris, GFTT, DoG, Fast-Hessian, FAST, CenSurE detectors and the SIFT, SURF, LIOP, ORB, BRISK, FREAK descriptors are benchmarked for images of Envisat. It was found that a combination of Fast-Hessian with BRISK was the most robust, while still capable of running on a low resolution and acquisition rate setup. For large baselines, the rate of false-positives increases, limiting their use in model-based strategies.
Open Access
Biologically-inspired machine vision
(2013-09-25) Tsitiridis, A.; Richardson, Mark A.
This thesis summarises research on the improved design, integration and expansion of past cortex-like computer vision models, following biologically-inspired methodologies. By adopting early theories and algorithms as a building block, particular interest has been shown for algorithmic parameterisation, feature extraction, invariance properties and classification. Overall, the major original contributions of this thesis have been: 1. The incorporation of a salient feature-based method for semantic feature extraction and refinement in object recognition. 2. The design and integration of colour features coupled with the existing morphological-based features for efficient and improved biologically-inspired object recognition. 3. The introduction of the illumination invariance property with colour constancy methods under a biologically-inspired framework. 4. The development and investigation of rotation invariance methods to improve robustness and compensate for the lack of such a mechanism in the original models. 5. Adaptive Gabor filter design that captures texture information, enhancing the morphological description of objects in a visual scene and improving the overall classification performance. 6. Instigation of pioneering research on Spiking Neural Network classification for biologically-inspired vision. Most of the above contributions have also been presented in two journal publications and five conference papers. The system has been fully developed and tested in computers using MATLAB under a variety of image datasets either created for the purposes of this work or obtained from the public domain.
Open Access
ChiNet: deep recurrent convolutional learning for multimodal spacecraft pose estimation
(IEEE, 2022-07-22) Rondao, Duarte; Aouf, Nabil; Richardson, Mark A.
This paper presents an innovative deep learning pipeline which estimates the relative pose of a spacecraft by incorporating the temporal information from a rendezvous sequence. It leverages the performance of long short-term memory (LSTM) units in modelling sequences of data for the processing of features extracted by a convolutional neural network (CNN) backbone. Three distinct training strategies, which follow a coarse-to-fine funnelled approach, are combined to facilitate feature learning and improve end-to-end pose estimation by regression. The capability of CNNs to autonomously ascertain feature representations from images is exploited to fuse thermal infrared data with electro-optical red-green-blue (RGB) inputs, thus mitigating the effects of artifacts from imaging space objects in the visible wavelength. Each contribution of the proposed framework, dubbed ChiNet, is demonstrated on a synthetic dataset, and the complete pipeline is validated on experimental data.
Open Access
Comparison of empirical and predicted ultraviolet aircraft signatures
(SPIE, 2019-02-09) James, Itor; Richardson, Mark A.; O'Keefe, Eoin
In light of the potential threat to aircraft from missiles using ultraviolet (UV) wavebands, it is important to understand the signature of an aircraft and how this can be predicted. This study compares empirical UV signature data to modeled data from camouflage electro-optical simulation (CAMEOSIM) to determine how well the contrast between the object and the background can be predicted using local knowledge of the atmosphere. CAMEOSIM uses the standard moderate resolution atmospheric transmission (MODTRAN) model to estimate the radiative transfer through the atmosphere. Both MODTRAN and CAMEOSIM are well validated in visible and infrared wavebands, and MODTRAN can accurately predict UV radiative transfer. Unfortunately, the work so far has concentrated on bulk transfer to describe the sky background in the UV where the aircraft scene is typically a negative contrast “hole” in a positive sky background. Importantly, path-to-path scattering is a key consideration in this scene since it is this that will tend to blur the edges of an object and reduce the contrast associated with it. A developed understanding of the limitations is required. It is determined that prediction is possible up to the ranges of 5 km. The local visibility (in km) is required for this prediction.
Open Access
DeepLO: Multi-projection deep LIDAR odometry for space orbital robotics rendezvous relative navigation
(Elsevier, 2020-07-30) Kechagias-Stamatis, Odysseas; Aouf, Nabil; Dubanchet, Vincent; Richardson, Mark A.
This work proposes a new Light Detection and Ranging (LIDAR) based navigation architecture that is appropriate for uncooperative relative robotic space navigation applications. In contrast to current solutions that exploit 3D LIDAR data, our architecture suggests a Deep Recurrent Convolutional Neural Network (DRCNN) that exploits multi-projected imagery of the acquired 3D LIDAR data. Advantages of the proposed DRCNN are; an effective feature representation facilitated by the Convolutional Neural Network module within DRCNN, a robust modeling of the navigation dynamics due to the Recurrent Neural Network incorporated in the DRCNN, and a low processing time. Our trials evaluate several current state-of-the-art space navigation methods on various simulated but credible scenarios that involve a satellite model developed by Thales Alenia Space (France). Additionally, we evaluate real satellite LIDAR data acquired in our lab. Results demonstrate that the proposed architecture, although trained solely on simulated data, is highly adaptable and is more appealing compared to current algorithms on both simulated and real LIDAR data scenarios affording better odometry accuracy at lower computational requirements.
Open Access
Design of a tunable snapshot multispectral imaging system through ray tracing simulation
(MDPI, 2019-01-05) Ding, Mengjia; Yuen, Peter W. T.; Piper, Jonathan; Godfree, Peter; Chatterjee, Ayan; Zahidi, Usman; Selvagumar, Senthurran; James, David; Richardson, Mark A.
Research on snapshot multispectral imaging has been popular in the remote sensing community due to the high demands of video-rate remote sensing system for various applications. Existing snapshot multispectral imaging techniques are mainly of a fixed wavelength type, which limits their practical usefulness. This paper describes a tunable multispectral snapshot system by using a dual prism assembly as the dispersion element of the coded aperture snapshot spectral imagers (CASSI). Spectral tuning is achieved by adjusting the air gap displacement of the dual prism assembly. Typical spectral shifts of about 1 nm at 400 nm and 12 nm at 700 nm wavelength have been achieved in the present design when the air-gap of the dual prism is changed from 4.24 mm to 5.04 mm. The paper outlines the optical designs, the performance, and the pros and cons of the dual-prism CASSI (DP-CASSI) system. The performance of the system is illustrated by TraceProTM ray tracing, to allow researchers in the field to repeat or to validate the results presented in this paper.
Open Access
Design of single prism coded aperture snapshot spectral imager using ray tracing simulation
(IEEE, 2019-03-07) Ding, Mengjia; Yuen, Peter W. T.; Richardson, Mark A.
Novel imaging systems published in the literature mostly concern with the performance of the final stage of the designed system which normally accompanies with a brief description of the system configuration only. Other information, such as how the system was optimized and the methodology adopted for improving them to their final stage are heavily lacking in the open domain. This paper addresses this issue by providing a guide for the modeling of compressive imaging based on Single Disperser Coded Aperture Snapshot Spectral Imaging (SD-CASSI), with focuses on the optimization of the dispersion capability, the reduction of spatial and chromatic aberrations for enhancing the performance of the SD-CASSI. As an example the system is designed for a numerical aperture of 0.125, 0.3% distortion at central wavelength 587.56 nm, and 32 spectral bands with a spatial resolution of 13 μm. The system was simulated by ray tracing program TracePro.
Open Access
Detection of psychological stress using a hyperspectral imaging technique
(IEEE, 2014-10-09) Chen, Tong; Yuen, Peter W. T.; Richardson, Mark A.; Liu, Guangyuan; She, Zhishun
The detection of stress at early stages is beneficial to both individuals and communities. However, traditional stress detection methods that use physiological signals are contact-based and require sensors to be in contact with test subjects for measurement. In this paper, we present a method to detect psychological stress in a non-contact manner using a human physiological response. In particular, we utilize a hyperspectral imaging (HSI) technique to extract the tissue oxygen saturation (StO2) value as a physiological feature for stress detection. Our experimental results indicate that this new feature may be independent from perspiration and ambient temperature. Trier Social Stress Tests (TSSTs) on 21 volunteers demonstrated a significant difference $p\< 0.005$ and a large practical discrimination (d 1/4 1.37) between normalized baseline and stress StO2 levels. The accuracy for stress recognition from baseline using a binary classifier was 76.19 and 88.1 percent for the automatic and manual selections of the classifier threshold, respectively. These results suggest that the StO2 level could serve as a new modality to recognize stress at standoff distances.
Open Access
Development of a Laser Test Range for the Italian Air Force: Airborne Laser Systems Performance Prediction, Safety Analysis, Flight Testing and Operational Training
(2008-11-25T15:16:17Z) Sabatini, Roberto; Richardson, Mark A.
This thesis describes the research work performed for designing, developing and testing a new laser test and training range for the Italian Air Force. This includes the design of new range instrumentation and facilities, development of innovative methods for military systems performance prediction/evaluation and determination of eye-safety requirements for employment of ground and airborne laser systems at the laser range (during both experimental and training activities), and extensive laboratory, ground and flight test activities with state-of-the-art ground/airborne laser systems and laser guided weapons. Between 1997 and 1998 the Italian Air Force Official Flight Test Centre (ItAF-OTC) set the requirements for upgrading the PISQ test/training range (Poligono Interforze del Salto di Quirra - Sardinia - Italy), adding new facilities for carrying out safe training and experimental activities with airborne and ground laser systems, together with LOW delivery tests. According to these initial requirements, the PILASTER (PISQ LASer Test and Evaluation Range) research and development program was divided in two different phases. The aim of the first phase of the program (1999-2002) was to provide an initial operational capability for carrying out, in fully safe conditions, ground tests and flight experimental activities (with related measurements and semi-automated data analysis), required for performance evaluation of military laser systems. The successive phase of the program (still ongoing) is aimed to implementing the PILASTER full operational capability, required for performing all laser test/training activities, including all mission planning and fully-automatedpost-mission data analysistasks (2002-2004). Implementation of suitable mathematical models for laser systems performance analysis (i.e., atmospheric propagation, mission geometry, target back-scattering, etc.) is an essential requirement of the PILASTER program, due to the need for 'realistic' simulation and mission planning, together with reliable post-mission data analysis at the vi range. Very important is also the definition of eye-safety criteria and procedures, since most of current laser systems operate in the near infrared, with considerable risk for the naked human eye. In this research, present laser technology status and future technology trends are investigated, with particular emphasis for the systems now in service or under development for the Italian Air Force. These include the Thompson Convertible Laser Designation Pod (CLDP), The ELOP Portable Laser Designator (PLD) system, Laser Guided Bombs (e.g., PAVEWAY 11, PAVEWAY III and Lizard), and the Marconi- Selenia Laser Obstacle Avoidance System (LOAS) for helicopters. Furthermore, suitable mathematical models for ground/airborne laser systems performance analysis and mission planning are presented, together with innovative methods for evaluating the hazards associated with the use of ground and airborne laser systems at the PILASTER range. Particularly, after describing the technical requirements and design characteristics of the PILASTER range instrumentation, safety issues of state-of-the-art ground/airborne laser systems are thoroughly investigated, in order to identify operational procedures and limitations for the safe employment of such equipment at the PILASTER range during execution of both test and training missions. Furthermore, various mathematical algorithms are presented, developed for the PILASTER simulation and mission planning tools, that allow a complete verification of laser-safety for ground and airborne laser systems. Extensive laboratory, ground and flight experimental activities is performed with both ground and airborne laser systems in order to test the various PILASTER laser range systems and to validate/refine the models developed for systems performance analysis and simulation. Furthermore, the LOAS system is tested both on the ground and in flight, in order to assess the system obstacle detection performance in various weather conditions, and the efficiency of the algorithms developed for obstacle classification and trajectory optimisation.
Open Access
DFSGD: Machine Learning Based Intrusion Detection for Resource Constrained Devices
(2019-12) Lee, Seo Jin; Chermak, Lounis; Richardson, Mark A.; Yoo, Paul D.; Asyhari, Taufiq
An ever increasing number of smart and mobile devices interconnected through wireless networks such as Internet of Things (IoT) and huge sensitive network data transmitted between them has raised security and privacy issues. Intrusion detection system (IDS) is known as an effective defence system and often, machine learning (ML) and its subfield deep learning (DL) methods are used for its development. However, IoT devices have limited computational resources such as limited energy source and computational power and thus, traditional IDS that require extensive computational resource are not suitable for running on such devices. Therefore, the aim of this research is to design and develop a lightweight ML-based IDS for the resource-constrained devices. The research proposes a lightweight ML-based IDS model based on Deep Feature Learning with Linear SVM and Gradient Descent optimisation (DFSGD) to deploy and run on resource-constrained devices by reducing the number of features through feature extraction and selection using a stacked autoencoder (SAE), mutual information (MI) and C4.5 wrapper. The DFSGD is trained on Aegean Wi-Fi Intrusion Dataset (AWID) to detect impersonation attack and utilises support vector machine (SVM) and gradient descent as the classifier and optimisation algorithm respectively. As one of the key contributions of this research, the features in AWID dataset utilised for the development of the model, were also investigated for its usability for further development of IDS. Finally, the DFSGD was run on Raspberry Pi to show its possible deployment on resource-constrained devices.
Open Access
Digital shoreline analysis system-based change detection along the highly eroding Krishna–Godavari delta front
(SPIE, 2017-08-24) Kallepalli, Akhil; Kakani, N. R.; James, David B.; Richardson, Mark A.
Coastal regions are highly vulnerable to rising sea levels due to global warming. Previous Intergovernmental Panel on Climate Change (2013) predictions of 26 to 82 cm global sea level rise are now considered conservative. Subsequent investigations predict much higher levels which would displace 10% of the world’s population living less than 10 m above sea level. Remote sensing and GIS technologies form the mainstay of models on coastal retreat and inundation to future sea-level rise. This study estimates the varying trends along the Krishna–Godavari (K–G) delta region. The rate of shoreline shift along the 330-km long K–G delta coast was estimated using satellite images between 1977 and 2008. With reference to a selected baseline from along an inland position, end point rate and net shoreline movement were calculated using a GIS-based digital shoreline analysis system. The results indicated a net loss of about 42.1 km 2 area during this 31-year period, which is in agreement with previous literature. Considering the nature of landforms and EPR, the future hazard line (or coastline) is predicted for the area; the predication indicates a net erosion of about 57.6 km 2 along the K–G delta coast by 2050 AD.
Open Access
Experimental flight testing of night vision imaging systems in military fighter aircraft
(ASTM International, 2013-10-26) Sabatini, Roberto; Richardson, Mark A.; Cantiello, Maurizio; Toscano, Mario; Fiorini, Pietro; Zammit-Mangion, David; Gardi, Alessandro
This paper describes the research and experimental flight test activities conducted by the Italian Air Force Official Test Centre (RSV), in collaboration with Alenia Aermacchi and Cranfield University, in order to confer night vision imaging systems (NVIS) capability to the Italian TORNADO Interdiction and Strike and Electronic Combat and Reconnaissance aircraft. The activities included design, development, test, and evaluation activities, including night vision goggle (NVG) integration, cockpit instruments, and external lighting modifications, as well as various ground test sessions and a total of 18 flight test sorties. RSV and Litton Precision Products were responsible for coordinating and conducting the installation of the internal and external lights. Particularly, an iterative process was established allowing in-site rapid correction of the major deficiencies encountered during the ground and flight test sessions. Both single-ship (day/night) and formation (night) flights were performed, with testing activities shared among the test crews involved, allowing for a redundant examination of the various test items by all participants. An innovative test matrix was developed and implemented by RSV for assessing the operational suitability and effectiveness of the various modifications implemented. Also important was the definition of test criteria for Pilot and Weapon Systems Officer workload assessment during the accomplishment of various operational tasks during NVG missions. Furthermore, the specific technical and operational elements required for evaluating the modified helmets were identified, allowing an exhaustive comparative evaluation of the two proposed solutions (i.e., HGU-55P and HGU-55G modified helmets). The initial compatibility problems encountered were progressively mitigated by incorporating modifications in both front and rear cockpits at various stages of the test campaign. This process allowed considerable enhancement of the TORNADO NVIS configuration, giving good medium- to high-level NVG operational capability to the aircraft. Further developments also include the internal/external lighting for the Italian TORNADO “Mid-Life Update” and other programs such as AMX aircraft internal/external light modification/testing and the activities addressing low-altitude NVG operations with fast jets (e.g., TORNADO, AMX, MB-339CD), with a major issue being the safe ejection of aircrew with NVG and NVG modified helmets. Two options have been identified for solving this problem, namely, the modification of the current Gentex HGU-55 helmets and the design of a new helmet incorporating a reliable NVG connection/disconnection device (i.e., a mechanical system fully integrated in the helmet frame) with embedded automatic disconnection capability in case of ejection. Other relevant issues to be accounted for in these new developments are the helmet dimensions and weight, the NVG usable field of view as a function of eye-relief distance, and the helmet's center of gravity (moment arms) with and without NVG (effect on aircrew fatigue during training and real operational missions)
Open Access
High-speed multi-dimensional relative navigation for uncooperative space objects
(Elsevier, 2019-05-03) Kechagias-Stamatis, Odysseas; Aouf, Nabil; Richardson, Mark A.
This work proposes a high-speed Light Detection and Ranging (LIDAR) based navigation architecture that is appropriate for uncooperative relative space navigation applications. In contrast to current solutions that exploit 3D LIDAR data, our architecture transforms the odometry problem from the 3D space into multiple 2.5D ones and completes the odometry problem by utilizing a recursive filtering scheme. Trials evaluate several current state-of-the-art 2D keypoint detection and local feature description methods as well as recursive filtering techniques on a number of simulated but credible scenarios that involve a satellite model developed by Thales Alenia Space (France). Most appealing performance is attained by the 2D keypoint detector Good Features to Track (GFFT) combined with the feature descriptor KAZE, that are further combined with either the H∞ or the Kalman recursive filter. Experimental results demonstrate that compared to current algorithms, the GFTT/KAZE combination is highly appealing affording one order of magnitude more accurate odometry and a very low processing burden, which depending on the competitor method, may exceed one order of magnitude faster computation.
Open Access
Hybrid CoAP-based resource discovery for the Internet of Things
(Springer, 2017-02-16) Djamaa, Badis; Yachir, Ali; Richardson, Mark A.
Enabling automatic, efficient and scalable discovery of the resources provided by constrained low-power sensor and actuator networks is an important element to empower the transformation towards the Internet of Things (IoT). To this end, many centralized and distributed resource discovery approaches have been investigated. Clearly, each approach has its own motivations, advantages and drawbacks. In this article, we present a hybrid centralized/distributed resource discovery solution aiming to get the most out of both approaches. The proposed architecture employs the well-known Constrained Application Protocol (CoAP) and features a number of interesting discovery characteristics including scalability, time and cost efficiency, and adaptability. Using such a solution, network nodes can automatically and rapidly detect the presence of Resource Directories (RDs), via a proactive RD discovery mechanism, and perform discovery tasks through them. Nodes may, alternatively, fall back automatically to efficient fully-distributed discovery operations achieved through Trickle-enabled, CoAP-based technics. The effectiveness of the proposed architecture has been demonstrated by formal analysis and experimental evaluations on dedicated IoT platforms.
Open Access
Illumination invariance and shadow compensation via spectro-polarimetry technique
(Maney Publishing, 2013-06-28T00:00:00Z) Ibrahim, I.; Yuen, Peter W. T.; Hong, K.; Chen, T.; Soori, U.; Jackman, J.; Richardson, Mark A.
A major problem for obtaining target reflectance via hyperspectral imaging systems is the presence of illumination and shadow effects. These factors are common artefacts, especially when dealing with a hyperspectral imaging system that has sensors in the visible to near infrared region. This region is known to have highly scattered and diffuse radiance which can modify the energy recorded by the imaging system. Shadow effect will lower the target reflectance values due to the small radiant energy impinging on the target surface. Combined with illumination artefacts, such as diffuse scattering from the surrounding targets, background or environment, the shape of the shadowed target reflectance will be altered. In this study we propose a new method to compensate for illumination and shadow effects on hyperspectral imageries by using a polarization technique. This technique, called spectro-polarimetry, estimates the direct and diffuse irradiance based on two images, taken with and without a polarizer. The method is evaluated using a spectral similarity measure, angle and distance metric. The results of indoor and outdoor tests have shown that using the spectro-polarimetry technique can improve the spectral constancy between shadow and full illumination spectra.
Open Access
Impact of channel number on architecture and performance of advanced processing techniques for highly channelised multichannel systems
(2020-07) McKelvey, Anthony Martin; Vagias, Ioannis; Richardson, Mark A.; May, Andrew
The effect of varying the number of digitised channels on a radar systems performance is analysed through modelling several spatial processing techniques and determining the effect of varying design. Performance is measured using several metrics with scan rate, minimum detectable velocity, and robustness to jamming being key. Modelling is carried out for a number of different operational scenarios and channel architectures. Through this modelling effort the effects of varying channel number on performance in different operational environments is determined. These outputs are weighed against limitations introduced by increasing channel number (e.g. increased computational complexity). These results are used to form conclusion on what number of channels provides the best overall performance gain. In the case of the radar system model in this thesis N=32 and N=16 channels have been determined to offer the best performance whilst minimising limiting factors.
Open Access
Implications of spectral and spatial features to improve the identification of specific classes
(SPIE, 2019-01-14) Kallepalli, Akhil; Kumar, Anil; Khoshelham, Kourosh; James, David B.; Richardson, Mark A.
Dimensionality is one of the greatest challenges when deciphering hyperspectral imaging data. Although the multiband nature of the data is beneficial, algorithms are faced with a high computational load and statistical incompatibility due to the insufficient number of training samples. This is a hurdle to downstream applications. The combination of dimensionality and the real-world scenario of mixed pixels makes the identification and classification of imaging data challenging. Here, we address the complications of dimensionality using specific spectral indices from band combinations and spatial indices from texture measures for classification to better identify the classes. We classified spectral and combined spatial–spectral data and calculated measures of accuracy and entropy. A reduction in entropy and an overall accuracy of 80.50% was achieved when using the spectral–spatial input, compared with 65% for the spectral indices alone and 59.50% for the optimally determined principal components.
Open Access
Local feature based automatic target recognition for future 3D active homing seeker missiles
(Elsevier, 2017-12-13) Kechagias-Stamatis, Odysseas; Aouf, Nabil; Gray, Greer Jillian; Chermak, Lounis; Richardson, Mark A.; Oudyi, F.
We propose an architecture appropriate for future Light Detection and Ranging (LIDAR) active homing seeker missiles with Automatic Target Recognition (ATR) capabilities. Our proposal enhances military targeting performance by extending ATR into the 3rd dimension. From a military and aerospace industry point of view, this is appealing as weapon effectiveness against camouflage, concealment and deception techniques can be substantially improved. Specifically, we present a missile seeker 3D ATR architecture that relies on the 3D local feature based SHOT descriptor and a dual-role pipeline with a number of pre and post-processing operations. We evaluate our architecture on a number of missile engagement scenarios in various environmental setups with the missile being under various altitudes, obliquities, distances to the target and scene resolutions. Under these demanding conditions, the recognition performance gained is highly promising. Even in the extreme case of reducing the database entries to a single template per target, our interchangeable ATR architecture still provides a highly acceptable performance. Although we focus on future intelligent missile systems, our approach can be implemented to a great range of time-critical complex systems for space, air and ground environments for military, law-enforcement, commercial and research purposes.
Open Access
Low-cost PC-based high-fidelity infrared signature modelling and simulation
(Cranfield University, 2008-08-13T15:36:10Z) Baqar, S.; Richardson, Mark A.
In the light of the increasing terrorist SAMs threat to civil and military aircraft, the need of a high-fidelity, low-cost, IR signature scene modelling and simulation capability that could be used for development, testing and evaluation of IRCM systems cannot be overlooked. The performance evaluation, training and testing of IR missiles or other IR based weapon systems, is very expensive and is also dependent upon atmospheric factors. Whereas, the computer based non-destructive simulation can provide a cost-effective alternative to field trials. An effort has been made to model the IR scene signature using virtual reality modelling tools and integrating this model into the missile-target engagement and countermeasure simulator. The developed algorithm can simulate passive IR imaging seeker engagements with aerial targets. The presented algorithm uses the developed models for IR signature of the target, the background, the flare spectral and temporal responses and the flare ballistic trajectory. The missile guidance, auto-pilot and tracker algorithms have also been developed. The atmospheric conditions have been modelled, using LOWTRAN, as “good”, “typical” or “bad” to account for atmospheric transmittance and the sky-radiance. The results were analysed and validated through four test scenarios. The code is written in MATLAB which gives it openness for user verification/validation and also flexibility for any future modifications. The work presented may help the IRCM designer and pilots to evaluate potential strategies to defeat the imaging seeker threat.