Networked fusion estimation with multiple uncertainties and time-correlated channel noise

This paper is concerned with the fusion filtering and fixed-point smoothing problems for a class of networked systems with multiple random uncertainties in both the sensor outputs and the transmission connections. To deal with this kind of systems, random parameter matrices are considered in the mathematical models of both the sensor measurements and the data available after transmission. The additive noise in the transmission channel from each sensor is assumed to be sequentially time-correlated. By using the time-differencing approach, the available measurements are transformed into an equivalent set of observations that do not depend on the timecorrelated noise. The innovation approach is then applied to obtain recursive distributed and centralized fusion estimation algorithms for the filtering and fixed-point smoothing estimators of the signal based on the transformed measurements, which are equal to the estimators based on the original ones. The derivation of the algorithms does not require the knowledge of the signal evolution model, but only the mean and covariance functions of the processes involved (covariance information). A simulation example illustrates the utility and effectiveness of the proposed fusion estimation algorithms, as well as the applicability of the current model to deal with different network-induced random phenomena.This research is supported by Ministerio de Economía, Industria y Competitividad, Agencia Estatal de Investigación and Fondo Europeo de Desarrollo Regional FEDER (grant no. MTM2017-84199-P)

Networked distributed fusion estimation under uncertain outputs with random transmission delays, packet losses and multi-packet processing

This paper investigates the distributed fusion estimation problem for networked systems whose mul- tisensor measured outputs involve uncertainties modelled by random parameter matrices. Each sensor transmits its measured outputs to a local processor over different communication channels and random failures –one-step delays and packet dropouts–are assumed to occur during the transmission. White sequences of Bernoulli random variables with different probabilities are introduced to describe the ob- servations that are used to update the estimators at each sampling time. Due to the transmission failures, each local processor may receive either one or two data packets, or even nothing and, when the current measurement does not arrive on time, its predictor is used in the design of the estimators to compensate the lack of updated information. By using an innovation approach, local least-squares linear estimators (filter and fixed-point smoother) are obtained at the individual local processors, without requiring the signal evolution model. From these local estimators, distributed fusion filtering and smoothing estimators weighted by matrices are obtained in a unified way, by applying the least-squares criterion. A simula- tion study is presented to examine the performance of the estimators and the influence that both sensor uncertainties and transmission failures have on the estimation accuracy.This research is supported by Ministerio de Economía, Industria y Competitividad, Agencia Estatal de Investigación and Fondo Europeo de Desarrollo Regional FEDER (grant no. MTM2017-84199-P)

Centralized Fusion Approach to the Estimation Problem with Multi-Packet Processing under Uncertainty in Outputs and Transmissions

This paper is concerned with the least-squares linear centralized estimation problem in multi-sensor network systems from measured outputs with uncertainties modeled by random parameter matrices. These measurements are transmitted to a central processor over different communication channels, and owing to the unreliability of the network, random one-step delays and packet dropouts are assumed to occur during the transmissions. In order to avoid network congestion, at each sampling time, each sensor’s data packet is transmitted just once, but due to the uncertainty of the transmissions, the processing center may receive either one packet, two packets, or nothing. Different white sequences of Bernoulli random variables are introduced to describe the observations used to update the estimators at each sampling time. To address the centralized estimation problem, augmented observation vectors are defined by accumulating the raw measurements from the different sensors, and when the current measurement of a sensor does not arrive on time, the corresponding component of the augmented measured output predictor is used as compensation in the estimator design. Through an innovation approach, centralized fusion estimators, including predictors, filters, and smoothers are obtained by recursive algorithms without requiring the signal evolution model. A numerical example is presented to show how uncertain systems with state-dependent multiplicative noise can be covered by the proposed model and how the estimation accuracy is influenced by both sensor uncertainties and transmission failures.This research is supported by Ministerio de Economía, Industria y Competitividad, Agencia Estatal de Investigación and Fondo Europeo de Desarrollo Regional FEDER (grant no. MTM2017-84199-P)

Multi-sensor multi-rate fusion estimation for networked systems: Advances and perspectives

National Natural Science Foundation of China under Grants 62103095, 61873058, 61873148 and 61933007; AHPU Youth Top-notch Talent Support Program of China under Grant 2018BJRC009; Natural Science Foundation of Anhui Province of China under Grant 2108085MA07; Royal Society of the UK; Alexander von Humboldt Foundation of Germany

Linear Estimation in Interconnected Sensor Systems with Information Constraints

A ubiquitous challenge in many technical applications is to estimate an unknown state by means of data that stems from several, often heterogeneous sensor sources. In this book, information is interpreted stochastically, and techniques for the distributed processing of data are derived that minimize the error of estimates about the unknown state. Methods for the reconstruction of dependencies are proposed and novel approaches for the distributed processing of noisy data are developed

Linear Estimation in Interconnected Sensor Systems with Information Constraints

A ubiquitous challenge in many technical applications is to estimate an unknown state by means of data that stems from several, often heterogeneous sensor sources. In this book, information is interpreted stochastically, and techniques for the distributed processing of data are derived that minimize the error of estimates about the unknown state. Methods for the reconstruction of dependencies are proposed and novel approaches for the distributed processing of noisy data are developed

Discrete Time Systems

Discrete-Time Systems comprehend an important and broad research field. The consolidation of digital-based computational means in the present, pushes a technological tool into the field with a tremendous impact in areas like Control, Signal Processing, Communications, System Modelling and related Applications. This book attempts to give a scope in the wide area of Discrete-Time Systems. Their contents are grouped conveniently in sections according to significant areas, namely Filtering, Fixed and Adaptive Control Systems, Stability Problems and Miscellaneous Applications. We think that the contribution of the book enlarges the field of the Discrete-Time Systems with signification in the present state-of-the-art. Despite the vertiginous advance in the field, we also believe that the topics described here allow us also to look through some main tendencies in the next years in the research area

Development and experimental validation of direct controller tuning for spaceborne telescopes

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2000.Includes bibliographical references (p. 285-294).Strict requirements in the performance of future space-based observatories such as the Space Interferometry Mission (SIM) and the Next Generation Space Telescope (NGST), will extend the state-of-the-art of critical mission spaceflight-proven active control design. A control design strategy, which combines the high performance and stability robustness guarantees of modem, robust-control design with the spaceflight heritage of conventional control design, is proposed which will meet the strict requirements and maintain traceability to the successful controllers from predecessor spacecraft. Two principal tools are developed: an analysis algorithm that quantifies each sensor/actuator combination's effectiveness for control, and a design engine which tunes a baseline controller to improve performance and/or stability robustness. The sensor/actuator effectiveness indexing tool requires a reduced-order state-space model of the plant. A modification of the balanced reduction method is introduced which improves numerical conditioning so that the order of large models of flexible spacecraft may be decreased. For each sensor and actuator an index is computed using the modal controllability from an actuator weighted by the modal cost in the performance, and the model observability of a sensor weighted by the modal cost of the disturbance. The special case of actuators that are used for active output isolation is handled separately. The designer makes use of the sensor/actuator indexing tool to select which control channels to emphasize in the tuning. The tuning tool is based on forming an augmented cost from weighting performance, stability robustness, deviation from the baseline controller, and controller gain. The tuning algorithm can operate with the plant's state-space design model or directly with the plant's measured frequency-response data. Two differentiable multivariable stability robustness metrics are formed, one based on the maximum singular value of the Sensitivity transfer matrix and one based on the multivariable Nyquist locus. The controller is parameterized with a general tridiagonal parameterization based on the real-modal state-space form. The augmented cost is chosen to be differentiable and a closed-loop stability-preserving unconstrained nonlinear descent program is used to directly compute controller parameters that decrease the augmented cost. To automate the closed-loop stability determination in the measured-data-based designs, a rule-based algorithm is created to invoke the multivariable Nyquist stability criteria. The use of the tuning technique is placed in context with a high-level control design methodology. The tuning technique is evaluated on a sample problem and then experimentally demonstrated on a laboratory test article with dynamics, sensor suite, and actuator suite all similar to future spaceborne observatories. The developed test article is the first spacetelescope- like experimental facility to combine large-angle slewing with nanometer optical phasing and sub-arcsecond pointing in the presence of spacecraft-like disturbances. The technique is applied to generate an improved controller for a model of the SIM spacecraft.by Gregory J.W. Mallory.Ph.D

Towards a robust slam framework for resilient AUV navigation

Autonomous Underwater Vehicles (AUVs) are playing an increasing part in modern navies, to the point that the control of oceans will soon be decided by their strategic use. In face of more complex missions occurring in potentially hostile environments, the resilience of such systems becomes critical. In this study, we investigate the following scenario: how does a lone AUV could recover from a temporary breakdown that has created a gap in its measurements, while remaining beneath the surface to avoid detection? It is assumed that the AUV is equipped with an active sonar and is operating in an uncharted area. The vehicle has to rely on itself by recovering its location using a Simultaneous Localization and Mapping (SLAM) algorithm. While SLAM is widely investigated and developed in the case of aerial and terrestrial robotics, the nature of the poorly structured underwater environment dramatically challenges its effectiveness. To address such a complex problem, the usual side scan sonar data association techniques are investigated under a global registration problem while applying robust graph SLAM modelling. In particular, ways to improve the global detection of features from sonar mosaic region patches that react well to the MICR similarity measure are discussed. The main contribution of this study is centered on a novel data processing framework that is able to generate different graph topologies using robust SLAM techniques. One of its advantages is to facilitate the testing of different modelling hypotheses to tackle the data gap following the temporary breakdown and make the most of the limited available information. Several research perspectives related to this framework are discussed. Notably, the possibility to further extend the proposed framework to heterogeneous datasets and the opportunity to accelerate the recovery process by inferring information about the breakdown using machine learning.PH

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas