Länsimaisten SAR/GMTI-tutkien suorituskyky

SAR/GMTI-tutka (Synthetic Aperture Radar/Ground Moving Target Indicator) tuottaa tiedustelutietoa johtamisen, tiedustelun, valvonnan ja maalinosoituksen tueksi. SAR/GMTI -tutka on luotettava tiedusteluväline tutkataajuusalueella tapahtuvan tiedustelutiedon tuottamisen ansiosta, jolloin tutkan käyttö ja tiedustelutiedon tuottaminen onnistuvat huonoissakin sääolosuhteissa. SAR/GMTI-tutkien käyttö on yleistynyt sotilaskäytössä viimeisen kahdenkymmenen vuoden aikana ja niillä on ollut suuri merkitys kaikissa suuremmissa konflikteissa kylmän sodan loppumisen jälkeen. SAR-tutka käyttää lavettina liikkuvaa alustaa, useimmiten lentokonetta, muodostaakseen virtuaalisen antenniryhmän ja signaaliprosessoinnin avulla, jolloin vastaanottimeen palautuvat kaiut sijoitetaan kohdilleen ja muodostetaan SAR-kuvaa. Tarkimmillaan nykyisten SARtutkien resoluutio on muutaman kymmenen senttimetrin luokkaa ja mittausetäisyydet suurimmillaan satoja kilometrejä. GMTI-tutka havaitsee liikkuvat kohteet, kun liikkuvista kohteista palautuvat kaiuilla on eri taajuus kuin ympäröivästä maastosta palautuvilla kaiuilla ja kohteet pystytään erottelemaan välkkeen seasta. GMTI-tutkan toiminta perustuu doppler-ilmiöön. SAR-tutkaa pystytään useimmiten käyttämään GMTI-moodissa. Sotilaskäytössä olevat GMTI-tutkat pystyvät havaitsemaan keskimäärin noin henkilöauton kokoisen maalin, joka liikkuu noin 5 km/h nopeudella. SAR/GMTI-tutkia on käytetty menestyksellisesti molemmissa Irakin sodissa Yhdysvaltojen toimesta, kun tasainen aavikko ei aiheuttanut juurikaan ongelmia alueen valvontaan ilmasta käsin. Sen sijaan haasteita SAR/GMTI-tutkille ovat aiheuttaneet operaatiot Balkanilla ja Afganistanissa korkean vuoriston, peitteisen maaston ja kohteiden hankalan tunnistettavuuden takia. Suoraan taistelun tukemiseen liittyen GMTI-tutkat ovat olleet hyödyllisiä, kun valvontakoneilta saadut tiedot liikkuvista vihollisosastoista on voitu lähettää datalinkkien kautta lähestulkoon reaaliajassa. SAR-tutkat ovat olleet hyödyllisiä ennen taisteluiden alkua tiedustelutiedon keräämisessä ja vaikeakulkuisessa maastossa SAR-tutkia on käytetty esimerkiksi taisteluvaikutuksen jälkiarviointiin. SAR/GMTI-tutkien suorituskyky kehittyy jatkuvasti laitteiden resoluution ja koon pienentyessä. Datalinkeillä voidaan välittää tietoa alajohtoportaille ja SAR/GMTI-tutkia on voitu sijoittaa esimerkiksi UAV-lennokkeihin (Unmanned Aerial Vehicle), joilla on voitu suorittaa tarkempaa aluevalvontaa kuin mitä isomman kokoluokan valvontakoneilla voitaisiin toteuttaa.

UAV:n soveltuvuus CAS-toimintaan

Tutkimuksen tavoitteena on selvittää, miten monikäyttöinen UAV (Unmanned Aerial Vehicle) soveltuisi CAS-tehtäviin. UAV-laitteita käytetään moniin eri tehtäviin niin siviili- kuin sotilasmaailmassakin, ja niiden kehittämiseen käytetään maailmanlaajuisesti vuosi vuodelta enemmän resursseja. Kokemusta niiden käytöstä teknisesti yhtä vahvaa vihollista vastaan ei juurikaan ole, minkä vuoksi tässä tutkimuksessa painotetaan toimimista vihollisen maajoukkojen välittömässä läheisyydessä ja lähdetään siitä oletuksesta, että vihollisella on liikkuvaa ilmatorjuntaa mukana. Tämä on ratkaiseva ero UAV:eiden tähänastiseen ilmasta maahan -vaikuttamiseen nähden. Tutkimusta varten on kerätty julkisista lähteistä tietoa nykyaikaisten järjestelmien ominai-suuksista ja suorituskyvystä, minkä jälkeen niitä on tarkasteltu yleisten taktisten periaatteiden näkökulmasta. Yleisiin taktisiin periaatteisiin liittyvä teoria perustuu Mika Huttusen kirjoittamaan MPKK:n Taktiikan laitoksen julkaisuun Monimutkainen taktiikka. Eri järjestelmien ominaisuuksia tarkastelemalla on selvitetty, onko jollain niistä kannattavaa tai edes mahdollista toteuttaa tutkitun kaltaista tehtävää. Tutkimuksessa havaittiin, että ainakin esimerkkijärjestelmissä on muutamia puutteita, joiden vuoksi ne eivät ole tällä hetkellä järkeviä vaihtoehtoja CAS-toimintaan. Tulevaisuudessa tekniikan kehittyessä on kuitenkin täysin mahdollista, että perinteisen maataistelukoneen sijasta UAV:ta aletaan hyödyntää lähitulituen antamisessa.

Small teleoperated vehicles: study of potential use cases and empirical prototyping

This thesis researches the current state of small teleoperated devices, the need for them and developing one. Small teleoperated devices give the possibility to perform tasks that are impossible or dangerous for humans. This work concentrates on small devices and cheap components and discloses one way of developing a teleoperated vehicle, but not necessarily the optimal way. Development and the current state of teleoperation were studied by a literature review, in which the data was searched from literature as well as from the Internet. The need for teleoperated devices was mapped through a survey, where 11 professionals from variating fields were interviewed how they could utilize a teleoperated devices and with what kind of features. Also, a prototype was built as a proof of concept of small teleoperated devices. The prototype is controlled by a single-board microcomputer that also streams video to the controlling device. The video can be viewed on a display or with a head mounted display.

Motion Segmentation And Meanshift Assisted Contour Refinement For Airborne Video

This paper presents methods for moving object detection in airborne video surveillance. The motion segmentation in the above scenario is usually difficult because of small size of the object, motion of camera, and inconsistency in detected object shape etc. Here we present a motion segmentation system for moving camera video, based on background subtraction. An adaptive background building is used to take advantage of creation of background based on most recent frame. Our proposed system suggests CPU efficient alternative for conventional batch processing based background subtraction systems. We further refine the segmented motion by meanshift based mode association.

Using appearance and context for outdoor scene object classification

We propose a probabilistic object classifier for outdoor scene analysis as a first step in solving the problem of scene context generation. The method begins with a top-down control, which uses the previously learned models (appearance and absolute location) to obtain an initial pixel-level classification. This information provides us the core of objects, which is used to acquire a more accurate object model. Therefore, their growing by specific active regions allows us to obtain an accurate recognition of known regions. Next, a stage of general segmentation provides the segmentation of unknown regions by a bottom-strategy. Finally, the last stage tries to perform a region fusion of known and unknown segmented objects. The result is both a segmentation of the image and a recognition of each segment as a given object class or as an unknown segmented object. Furthermore, experimental results are shown and evaluated to prove the validity of our proposal

Hybrid coordination of reinforcement learning-based behaviors for AUV control

This paper proposes a hybrid coordination method for behavior-based control architectures. The hybrid method takes advantages of the robustness and modularity in competitive approaches as well as optimized trajectories in cooperative ones. This paper shows the feasibility of applying this hybrid method with a 3D-navigation to an autonomous underwater vehicle (AUV). The behaviors are learnt online by means of reinforcement learning. A continuous Q-learning implemented with a feed-forward neural network is employed. Realistic simulations were carried out. The results obtained show the good performance of the hybrid method on behavior coordination as well as the convergence of the behaviors

Augmented state Kalman filtering for AUV navigation

Addresses the problem of estimating the motion of an autonomous underwater vehicle (AUV), while it constructs a visual map ("mosaic" image) of the ocean floor. The vehicle is equipped with a down-looking camera which is used to compute its motion with respect to the seafloor. As the mosaic increases in size, a systematic bias is introduced in the alignment of the images which form the mosaic. Therefore, this accumulative error produces a drift in the estimation of the position of the vehicle. When the arbitrary trajectory of the AUV crosses over itself, it is possible to reduce this propagation of image alignment errors within the mosaic. A Kalman filter with augmented state is proposed to optimally estimate both the visual map and the vehicle position

Probabilistic sonar scan matching for an AUV

This paper proposes MSISpIC, a probabilistic sonar scan matching algorithm for the localization of an autonomous underwater vehicle (AUV). The technique uses range scans gathered with a Mechanical Scanning Imaging Sonar (MSIS), the robot displacement estimated through dead-reckoning using a Doppler velocity log (DVL) and a motion reference unit (MRU). The proposed method is an extension of the pIC algorithm. An extended Kalman filter (EKF) is used to estimate the robot-path during the scan in order to reference all the range and bearing measurements as well as their uncertainty to a scan fixed frame before registering. The major contribution consists of experimentally proving that probabilistic sonar scan matching techniques have the potential to improve the DVL-based navigation. The algorithm has been tested on an AUV guided along a 600 m path within an abandoned marina underwater environment with satisfactory results

Pose-based SLAM with probabilistic scan matching algorithm using a mechanical scanned imaging sonar

This paper proposes a pose-based algorithm to solve the full SLAM problem for an autonomous underwater vehicle (AUV), navigating in an unknown and possibly unstructured environment. The technique incorporate probabilistic scan matching with range scans gathered from a mechanical scanning imaging sonar (MSIS) and the robot dead-reckoning displacements estimated from a Doppler velocity log (DVL) and a motion reference unit (MRU). The proposed method utilizes two extended Kalman filters (EKF). The first, estimates the local path travelled by the robot while grabbing the scan as well as its uncertainty and provides position estimates for correcting the distortions that the vehicle motion produces in the acoustic images. The second is an augment state EKF that estimates and keeps the registered scans poses. The raw data from the sensors are processed and fused in-line. No priory structural information or initial pose are considered. The algorithm has been tested on an AUV guided along a 600 m path within a marina environment, showing the viability of the proposed approach

Mission control system for dam inspection with an AUV

This paper presents a complete control architecture that has been designed to fulfill predefined missions with an autonomous underwater vehicle (AUV). The control architecture has three levels of control: mission level, task level and vehicle level. The novelty of the work resides in the mission level, which is built with a Petri network that defines the sequence of tasks that are executed depending on the unpredictable situations that may occur. The task control system is composed of a set of active behaviours and a coordinator that selects the most appropriate vehicle action at each moment. The paper focuses on the design of the mission controller and its interaction with the task controller. Simulations, inspired on an industrial underwater inspection of a dam grate, show the effectiveness of the control architecture

ICTINEUAUV wins the First SAUC-E competition

A pioneer team of students of the University of Girona decided to design and develop an autonomous underwater vehicle (AUV) called ICTINEU-AUV to face the Student Autonomous Underwater Challenge-Europe (SAUC-E). The prototype has evolved from the initial computer aided design (CAD) model to become an operative AUV in the short period of seven months. The open frame and modular design principles together with the compatibility with other robots previously developed at the lab have provided the main design philosophy. Hence, at the robot's core, two networked computers give access to a wide set of sensors and actuators. The Gentoo/Linux distribution was chosen as the onboard operating system. A software architecture based on a set of distributed objects with soft real time capabilities was developed and a hybrid control architecture including mission control, a behavioural layer and a robust map-based localization algorithm made ICTINEU-AUV the winning entry

Behaviours implemented using a vision system based on coded light projection

In a search for new sensor systems and new methods for underwater vehicle positioning based on visual observation, this paper presents a computer vision system based on coded light projection. 3D information is taken from an underwater scene. This information is used to test obstacle avoidance behaviour. In addition, the main ideas for achieving stabilisation of the vehicle in front of an object are presented

On AUV control architecture

This paper surveys control architectures proposed in the literature and describes a control architecture that is being developed for a semi-autonomous underwater vehicle for intervention missions (SAUVIM) at the University of Hawaii. Conceived as hybrid, this architecture has been organized in three layers: planning, control and execution. The mission is planned with a sequence of subgoals. Each subgoal has a related task supervisor responsible for arranging a set of pre-programmed task modules in order to achieve the subgoal. Task modules are the key concept of the architecture. They are the main building blocks and can be dynamically re-arranged by the task supervisor. In our architecture, deliberation takes place at the planning layer while reaction is dealt through the parallel execution of the task modules. Hence, the system presents both a hierarchical and an heterarchical decomposition, being able to show a predictable response while keeping rapid reactivity to the dynamic environment

Selective submap joining SLAM for autonomous vehicles

Simultaneous Localization and Mapping (SLAM) do not result in consistent maps of large areas because of gradual increase of the uncertainty for long term missions. In addition, as the size of the map grows the computational cost increases, making SLAM solutions unsuitable for on-line applications. This thesis surveys SLAM approaches paying special attention to those approaches aimed to work on large scenarios. Special focus is given to existing underwater SLAM applications. A technique based on using independent local maps together with a global stochastic map is presented. This technique is called Selective Submap Joining SLAM (SSJS). A global map contains relative transformations between local maps, which are updated once a new loop is detected. Maps sharing several features are fused, maintaining the correlation between landmarks and vehicle's pose. The use of local maps reduces computational costs and improves map consistency as compared to state of the art techniques.

Adaptation of an commercially available stabilised R/C helicopter to a fully autonomous surveillance UAV

This paper presents the development of an autonomous surveillance UAV that competed in the Ministry of Defence Grand Challenge 2008. In order to focus on higher-level mission control, the UAV is built upon an existing commercially available stabilised R/C helicopter platform. The hardware architecture is developed to allow for non-invasion integration with the existing stabilised platform, and to enable to the distributed processing of closed loop control and mission goals. The resulting control system proved highly successful and was capable of flying within 40knott gusts. The software and safety architectures were key to the success of the research and also hold the potential for use in the development of more complex system comprising of multiple UAVs.