Efficient Multilateration Tracking with Concurrent Offset Estimation using Stochastic Filtering Techniques

Multilateration systems operate by deter- mining distances between a signal transmitter and a number of receivers. In aerial surveillance, radio sig- nals are emitted as Secondary Surveillance Radar (SSR) by the aircraft, representing the signal transmitter. A number of base stations (sensors) receive the signals at different times. Most common approaches use time dif- ference of arrival (TDOA) measurements, calculated by subtracting receiving times of one receiver from another. As TDOAs require intersecting hyperboloids, which is considered a hard task, this paper follows a different ap- proach, using raw receiving times. Thus, estimating the signal\u27s emission time is required, captured as a com- mon offset within an augmented version of the system state. This way, the multilateration problem is reduced to intersecting cones. Estimation of the aircraft\u27s posi- tion based on a nonlinear measurement model and an underlying linear system model is achieved using a lin- ear regression Kalman filter [1, 2]. A decomposed com- putation of the filter step is introduced, allowing a more efficient calculation

Wireless Acoustic Tracking for Extended Range Telepresence

Telepresence systems enable a user to experience virtual or distant environments by providing sensory feedback. Appropriate devices include head mounted displays (HMD) for visual perception, headphones for auditory response, or even haptic displays for tactile sensation and force feedback. While most common designs use dedicated input devices like joysticks or a space mouse, the approach followed in the present work takes the user\u27s position and viewing direction as an input, as he walks freely in his local surroundings. This is achieved by using acoustic tracking, where the user\u27s pose (position and orientation) is estimated on the basis of ranges measured between a set of wall-fastened loudspeakers and a microphone array fixed on the user\u27s HMD. To allow for natural user motion, a wearable, fully wireless telepresence system is introduced. The increase in comfort compared to wired solutions is obvious, as the user\u27s awareness of distracting cables is taken away during walking. Also the lightweight design and small dimensions contribute to ergonomics, as the whole assembly fits well into a small backpack

Cooperative localisation in underwater robotic swarms for ocean bottom seismic imaging.

Spatial information must be collected alongside the data modality of interest in wide variety of sub-sea applications, such as deep sea exploration, environmental monitoring, geological and ecological research, and samples collection. Ocean-bottom seismic surveys are vital for oil and gas exploration, and for productivity enhancement of an existing production facility. Ocean-bottom seismic sensors are deployed on the seabed to acquire those surveys. Node deployment methods used in industry today are costly, time-consuming and unusable in deep oceans. This study proposes the autonomous deployment of ocean-bottom seismic nodes, implemented by a swarm of Autonomous Underwater Vehicles (AUVs). In autonomous deployment of ocean-bottom seismic nodes, a swarm of sensor-equipped AUVs are deployed to achieve ocean-bottom seismic imaging through collaboration and communication. However, the severely limited bandwidth of underwater acoustic communications and the high cost of maritime assets limit the number of AUVs that can be deployed for experiments. A holistic fuzzy-based localisation framework for large underwater robotic swarms (i.e. with hundreds of AUVs) to dynamically fuse multiple position estimates of an autonomous underwater vehicle is proposed. Simplicity, exibility and scalability are the main three advantages inherent in the proposed localisation framework, when compared to other traditional and commonly adopted underwater localisation methods, such as the Extended Kalman Filter. The proposed fuzzy-based localisation algorithm improves the entire swarm mean localisation error and standard deviation (by 16.53% and 35.17% respectively) at a swarm size of 150 AUVs when compared to the Extended Kalman Filter based localisation with round-robin scheduling. The proposed fuzzy based localisation method requires fuzzy rules and fuzzy set parameters tuning, if the deployment scenario is changed. Therefore a cooperative localisation scheme that relies on a scalar localisation confidence value is proposed. A swarm subset is navigationally aided by ultra-short baseline and a swarm subset (i.e. navigation beacons) is configured to broadcast navigation aids (i.e. range-only), once their confidence values are higher than a predetermined confidence threshold. The confidence value and navigation beacons subset size are two key parameters for the proposed algorithm, so that they are optimised using the evolutionary multi-objective optimisation algorithm NSGA-II to enhance its localisation performance. Confidence value-based localisation is proposed to control the cooperation dynamics among the swarm agents, in terms of aiding acoustic exteroceptive sensors. Given the error characteristics of a commercially available ultra-short baseline system and the covariance matrix of a trilaterated underwater vehicle position, dead reckoning navigation - aided by Extended Kalman Filter-based acoustic exteroceptive sensors - is performed and controlled by the vehicle's confidence value. The proposed confidence-based localisation algorithm has significantly improved the entire swarm mean localisation error when compared to the fuzzy-based and round-robin Extended Kalman Filter-based localisation methods (by 67.10% and 59.28% respectively, at a swarm size of 150 AUVs). The proposed fuzzy-based and confidence-based localisation algorithms for cooperative underwater robotic swarms are validated on a co-simulation platform. A physics-based co-simulation platform that considers an environment's hydrodynamics, industrial grade inertial measurement unit and underwater acoustic communications characteristics is implemented for validation and optimisation purposes

Safety‐oriented discrete event model for airport A‐SMGCS reliability assessment

A detailed analysis of State of the Art Technologies and Procedures into Airport Advanced-Surface Movement Guidance and Control Systems has been provided in this thesis, together with the review ofStatistical Monte Carlo Analysis, Reliability Assessment and Petri Nets theories. This practical and theoretical background has lead the author to the conclusion that there is a lack of linkage in between these fields. At the same of time the rapid increasing of Air Traffic all over the world, has brought in evidence the urgent need of practical instruments able to identify and quantify the risks connected with Aircraft operations on the ground, since the Airport has shown to be the actual ‘bottle neck’ of the entire Air Transport System. Therefore, the only winning approach to such a critical matter has to be multi-disciplinary, sewing together apparently different subjects, coming from the most disparate areas of interest and trying to fulfil the gap. The result of this thesis work has come to a start towards the end, when a Timed Coloured Petri Net (TCPN) model of a ‘sample’ Airport A-SMGCS has been developed, that is capable of taking into account different orders of questions arisen during these recent years and tries to give them some good answers. The A-SMGCS Airport model is, in the end, a parametric tool relying on Discrete Event System theory, able to perform a Reliability Analysis of the system itself, that: • uses a Monte Carlo Analysis applied to a Timed Coloured Petri Net, whose purpose is to evaluate the Safety Level of Surface Movements along an Airport • lets the user to analyse the impact of Procedures and Reliability Indexes of Systems such as Surface Movement Radars, Automatic Dependent Surveillance-Broadcast, Airport Lighting Systems, Microwave Sensors, and so on… onto the Safety Level of Airport Aircraft Transport System • not only is a valid instrument in the Design Phase, but it is useful also into the Certifying Activities an in monitoring the Safety Level of the above mentioned System with respect to changes to Technologies and different Procedures.This TCPN model has been verified against qualitative engineering expectations by using simulation experiments and occupancy time schedules generated a priori. Simulation times are good, and since the model has been written into Simulink/Stateflow programming language, it can be compiled to run real-time in C language (Real-time workshop and Stateflow Coder), thus relying on portable code, able to run virtually on any platform, giving even better performances in terms of execution time. One of the most interesting applications of this work is the estimate, for an Airport, of the kind of A-SMGCS level of implementation needed (Technical/Economical convenience evaluation). As a matter of fact, starting from the Traffic Volume and choosing the kind of Ground Equipment to be installed, one can make predictions about the Safety Level of the System: if the value is compliant with the TLS required by ICAO, the A-SMGCS level of Implementation is sufficiently adequate. Nevertheless, even if the Level of Safety has been satisfied, some delays due to reduced or simplified performances (even if Safety is compliant) of some of the equipment (e.g. with reference to False Alarm Rates) can lead to previously unexpected economical consequences, thus requiring more accurate systems to be installed, in order to meet also Airport economical constraints. Work in progress includes the analysis of the effect of weather conditions and re-sequencing of a given schedule. The effect of re-sequencing a given schedule is not yet enough realistic since the model does not apply inter arrival and departure separations. However, the model might show some effect on different sequences based on runway occupancy times. A further developed model containing wake turbulence separation conditions would be more sensitive for this case. Hence, further work will be directed towards: • The development of On-Line Re-Scheduling based on the available actual runway/taxiway configuration and weather conditions. • The Engineering Safety Assessment of some small Italian Airport A-SMGCSs (Model validation with real data). • The application of Stochastic Differential Equations systems in order to evaluate the collision risk on the ground inside the Place alone on the Petri Net, in the event of a Short Term Conflict Alert (STCA), by adopting Reich Collision Risk Model. • Optimal Air Traffic Control Algorithms Synthesis (Adaptive look-ahead Optimization), by Dynamically Timed Coloured Petri Nets, together with the implementation of Error-Recovery Strategies and Diagnosis Functions

Localization in Wireless Sensor Networks

International audienceWith the proliferation of Wireless Sensor Networks (WSN) applications, knowing the node current location have become a crucial requirement. Location awareness enables various applications from object tracking to event monitoring, and also supports core network services such as: routing, topology control, coverage, boundary detection and clustering. Therefore, WSN localization have become an important area that attracted significant research interest. In the most common case, position related parameters are first extracted from the received measurements, and then used in a second step for estimating the position of the tracked node by means of a specific algorithm. From this perspective, this chapter is intended to provide an overview of the major localization techniques, in order to provide the reader with the necessary inputs to quickly understand the state-of-the-art and/or apply these techniques to localization problems such as robot networks. We first review the most common measurement techniques, and study their theoretical accuracy limits in terms of Cramer-Rao lower bounds. Secondly, we classify the main localization algorithms, taking those measurements as input in order to provide an estimated position of the tracked node(s)

Mapping and Merging Using Sound and Vision : Automatic Calibration and Map Fusion with Statistical Deformations

Over the last couple of years both cameras, audio and radio sensors have become cheaper and more common in our everyday lives. Such sensors can be used to create maps of where the sensors are positioned and the appearance of the surroundings. For sound and radio, the process of estimating the sender and receiver positions from time of arrival (TOA) or time-difference of arrival (TDOA) measurements is referred to as automatic calibration. The corresponding process for images is to estimate the camera positions as well as the positions of the objects captured in the images. This is called structure from motion (SfM) or visual simultaneous localisation and mapping (SLAM). In this thesis we present studies on how to create such maps, divided into three parts: to find accurate measurements; robust mapping; and merging of maps.The first part is treated in Paper I and involves finding precise – on a subsample level – TDOA measurements. These types of subsample refinements give a high precision, but are sensitive to noise. We present an explicit expression for the variance of the TDOA estimate and study the impact that noise in the signals has. Exact measurements is an important foundation for creating accurate maps. The second part of this thesis includes Papers II–V and covers the topic of robust self-calibration using one-dimensional signals, such as sound or radio. We estimate both sender and receiver positions using TOA and TDOA measurements. The estimation process is divided in two parts, where the first is specific for TOA or TDOA and involves solving a relaxed version of the problem. The second step is common for different types of problems and involves an upgrade from the relaxed solution to the sought parameters. In this thesis we present numerically stable minimal solvers for both these steps for some different setups with senders and receivers. We also suggest frameworks for how to use these solvers together with RANSAC to achieve systems that are robust to outliers, noise and missing data. Additionally, in the last paper we focus on extending self-calibration results, especially for the sound source path, which often cannot be fully reconstructed immediately. The third part of the thesis, Papers VI–VIII, is concerned with the merging of already estimated maps. We mainly focus on maps created from image data, but the methods are applicable to sparse 3D maps coming from different sensor modalities. Merging of maps can be advantageous if there are several map representations of the same environment, or if there is a need for adding new information to an already existing map. We suggest a compact map representation with a small memory footprint, which we then use to fuse maps efficiently. We suggest one method for fusion of maps that are pre-aligned, and one where we additionally estimate the coordinate system. The merging utilises a compact approximation of the residuals and allows for deformations in the original maps. Furthermore, we present minimal solvers for 3D point matching with statistical deformations – which increases the number of inliers when the original maps contain errors

Time-based Location Techniques Using Inexpensive, Unsynchronized Clocks in Wireless Networks

The ability to measure location using time of flight in IEEE 802.11 networks is impeded by the standard clock resolution, imprecise synchronization of the 802.11 protocol, and the inaccuracy of available clocks. To achieve real-time location with accuracy goals of a few meters, we derive new consensus synchronization techniques for free-running clocks. Using consensus synchronization, we improve existing time of arrival (TOA) techniques and introduce new time difference of arrival (TDOA) techniques. With this common basis, we show how TOA is theoretically superior to TDOA. Using TOA measurements, we can locate wireless nodes that participate in the location system, and using TDOA measurements, we can locate nodes that do not participate. We demonstrate applications using off-the-shelf 802.11 hardware that can determine location to within 3m using simple, existing optimization methods. The synchronization techniques extend existing ones providing distributed synchronization for free-running clocks to cases where send times cannot be controlled and adjusted precisely, as in 802.11 networks. These location and synchronization techniques may be applied to transmitting wireless nodes using any communication protocol where cooperating nodes can produce send and receive timestamps

Robust, Energy-Efficient, and Scalable Indoor Localization with Ultra-Wideband Technology

Ultra-wideband (UWB) technology has been rediscovered in recent years for its potential to provide centimeter-level accuracy in GNSS-denied environments. The large-scale adoption of UWB chipsets in smartphones brings demanding needs on the energy-efficiency, robustness, scalability, and crossdevice compatibility of UWB localization systems. This thesis investigates, characterizes, and proposes several solutions for these pressing concerns. First, we investigate the impact of different UWB device architectures on the energy efficiency, accuracy, and cross-platform compatibility of UWB localization systems. The thesis provides the first comprehensive comparison between the two types of physical interfaces (PHYs) defined in the IEEE 802.15.4 standard: with low and high pulse repetition frequency (LRP and HRP, respectively). In the comparison, we focus not only on the ranging/localization accuracy but also on the energy efficiency of the PHYs. We found that the LRP PHY consumes between 6.4–100 times less energy than the HRP PHY in the evaluated devices. On the other hand, distance measurements acquired with the HRP devices had 1.23–2 times lower standard deviation than those acquired with the LRP devices. Therefore, the HRP PHY might be more suitable for applications with high-accuracy constraints than the LRP PHY. The impact of different UWB PHYs also extends to the application layer. We found that ranging or localization error-mitigation techniques are frequently trained and tested on only one device and would likely not generalize to different platforms. To this end, we identified four challenges in developing platform-independent error-mitigation techniques in UWB localization, which can guide future research in this direction. Besides the cross-platform compatibility, localization error-mitigation techniques raise another concern: most of them rely on extensive data sets for training and testing. Such data sets are difficult and expensive to collect and often representative only of the precise environment they were collected in. We propose a method to detect and mitigate non-line-of-sight (NLOS) measurements that does not require any manually-collected data sets. Instead, the proposed method automatically labels incoming distance measurements based on their distance residuals during the localization process. The proposed detection and mitigation method reduces, on average, the mean and standard deviation of localization errors by 2.2 and 5.8 times, respectively. UWB and Bluetooth Low Energy (BLE) are frequently integrated in localization solutions since they can provide complementary functionalities: BLE is more energy-efficient than UWB but it can provide location estimates with only meter-level accuracy. On the other hand, UWB can localize targets with centimeter-level accuracy albeit with higher energy consumption than BLE. In this thesis, we provide a comprehensive study of the sources of instabilities in received signal strength (RSS) measurements acquired with BLE devices. The study can be used as a starting point for future research into BLE-based ranging techniques, as well as a benchmark for hybrid UWB–BLE localization systems. Finally, we propose a flexible scheduling scheme for time-difference of arrival (TDOA) localization with UWB devices. Unlike in previous approaches, the reference anchor and the order of the responding anchors changes every time slot. The flexible anchor allocation makes the system more robust to NLOS propagation than traditional approaches. In the proposed setup, the user device is a passive listener which localizes itself using messages received from the anchors. Therefore, the system can scale with an unlimited number of devices and can preserve the location privacy of the user. The proposed method is implemented on custom hardware using a commercial UWB chipset. We evaluated the proposed method against the standard TDOA algorithm and range-based localization. In line of sight (LOS), the proposed TDOA method has a localization accuracy similar to the standard TDOA algorithm, down to a 95% localization error of 15.9 cm. In NLOS, the proposed TDOA method outperforms the classic TDOA method in all scenarios, with a reduction of up to 16.4 cm in the localization error.Cotutelle -yhteisväitöskirj