126 research outputs found
Classification-Aided Robust Multiple Target Tracking Using Neural Enhanced Message Passing
We address the challenge of tracking an unknown number of targets in strong
clutter environments using measurements from a radar sensor. Leveraging the
range-Doppler spectra information, we identify the measurement classes, which
serve as additional information to enhance clutter rejection and data
association, thus bolstering the robustness of target tracking. We first
introduce a novel neural enhanced message passing approach, where the beliefs
obtained by the unified message passing are fed into the neural network as
additional information. The output beliefs are then utilized to refine the
original beliefs. Then, we propose a classification-aided robust multiple
target tracking algorithm, employing the neural enhanced message passing
technique. This algorithm is comprised of three modules: a message-passing
module, a neural network module, and a Dempster-Shafer module. The
message-passing module is used to represent the statistical model by the factor
graph and infers target kinematic states, visibility states, and data
associations based on the spatial measurement information. The neural network
module is employed to extract features from range-Doppler spectra and derive
beliefs on whether a measurement is target-generated or clutter-generated. The
Dempster-Shafer module is used to fuse the beliefs obtained from both the
factor graph and the neural network. As a result, our proposed algorithm adopts
a model-and-data-driven framework, effectively enhancing clutter suppression
and data association, leading to significant improvements in multiple target
tracking performance. We validate the effectiveness of our approach using both
simulated and real data scenarios, demonstrating its capability to handle
challenging tracking scenarios in practical radar applications.Comment: 15 page
Poisson Multi-Bernoulli Mixtures for Multiple Object Tracking
Multi-object tracking (MOT) refers to the process of estimating object trajectories of interest based on sequences of noisy sensor measurements obtained from multiple sources. Nowadays, MOT has found applications in numerous areas, including, e.g., air traffic control, maritime navigation, remote sensing, intelligent video surveillance, and more recently environmental perception, which is a key enabling technology in automated vehicles. This thesis studies Poisson multi-Bernoulli mixture (PMBM) conjugate priors for MOT. Finite Set Statistics provides an elegant Bayesian formulation of MOT based on random finite sets (RFSs), and a significant trend in RFSs-based MOT is the development of conjugate distributions in Bayesian probability theory, such as the PMBM distributions. Multi-object conjugate priors are of great interest as they provide families of distributions that are suitable to work with when seeking accurate approximations to the true posterior distributions. Many RFS-based MOT approaches are only concerned with multi-object filtering without attempting to estimate object trajectories. An appealing approach to building trajectories is by computing the multi-object densities on sets of trajectories. This leads to the development of many multi-object filters based on sets of trajectories, e.g., the trajectory PMBM filters. In this thesis, [Paper A] and [Paper B] consider the problem of point object tracking where an object generates at most one measurement per time scan. In [Paper A], a multi-scan implementation of trajectory PMBM filters via dual decomposition is presented. In [Paper B], a multi-trajectory particle smoother using backward simulation is presented for computing the multi-object posterior for sets of trajectories using a sequence of multi-object filtering densities and a multi-object dynamic model. [Paper C] and [Paper D] consider the problem of extended object tracking where an object may generate multiple measurements per time scan. In [Paper C], an extended object Poisson multi-Bernoulli (PMB) filter is presented, where the PMBM posterior density after the update step is approximated as a PMB. In [Paper D], a trajectory PMB filter for extended object tracking using belief propagation is presented, where the efficient PMB approximation is enabled by leveraging the PMBM conjugacy and the factor graph formulation
A Poisson Multi-Bernoulli Mixture Filter for Coexisting Point and Extended Targets
This paper proposes a Poisson multi-Bernoulli mixture (PMBM) filter for coexisting point and extended targets, i.e., for scenarios where there may be simultaneous point and extended targets. The PMBM filter provides a recursion to compute the multi-target filtering posterior based on probabilistic information on data associations, and single-target predictions and updates. In this paper, we first derive the PMBM filter update for a generalised measurement model, which can include measurements originated from point and extended targets. Second, we propose a single-target space that accommodates both point and extended targets and derive the filtering recursion that propagates Gaussian densities for single targets and gamma Gaussian inverse Wishart densities for extended targets. As a computationally efficient approximation of the PMBM filter, we also develop a Poisson multi-Bernoulli (PMB) filter for coexisting point and extended targets. The resulting filters are analysed via numerical simulations
Robust GNSS Carrier Phase-based Position and Attitude Estimation Theory and Applications
Mención Internacional en el título de doctorNavigation information is an essential element for the functioning of robotic platforms and
intelligent transportation systems. Among the existing technologies, Global Navigation Satellite
Systems (GNSS) have established as the cornerstone for outdoor navigation, allowing for
all-weather, all-time positioning and timing at a worldwide scale. GNSS is the generic term
for referring to a constellation of satellites which transmit radio signals used primarily for
ranging information. Therefore, the successful operation and deployment of prospective
autonomous systems is subject to our capabilities to support GNSS in the provision of
robust and precise navigational estimates.
GNSS signals enable two types of ranging observations: –code pseudorange, which is a
measure of the time difference between the signal’s emission and reception at the satellite
and receiver, respectively, scaled by the speed of light; –carrier phase pseudorange, which
measures the beat of the carrier signal and the number of accumulated full carrier cycles.
While code pseudoranges provides an unambiguous measure of the distance between satellites
and receiver, with a dm-level precision when disregarding atmospheric delays and clock offsets,
carrier phase measurements present a much higher precision, at the cost of being ambiguous by
an unknown number of integer cycles, commonly denoted as ambiguities. Thus, the maximum
potential of GNSS, in terms of navigational precision, can be reach by the use of carrier phase
observations which, in turn, lead to complicated estimation problems.
This thesis deals with the estimation theory behind the provision of carrier phase-based
precise navigation for vehicles traversing scenarios with harsh signal propagation conditions.
Contributions to such a broad topic are made in three directions. First, the ultimate positioning
performance is addressed, by proposing lower bounds on the signal processing realized at the
receiver level and for the mixed real- and integer-valued problem related to carrier phase-based
positioning. Second, multi-antenna configurations are considered for the computation of a
vehicle’s orientation, introducing a new model for the joint position and attitude estimation
problems and proposing new deterministic and recursive estimators based on Lie Theory.
Finally, the framework of robust statistics is explored to propose new solutions to code- and
carrier phase-based navigation, able to deal with outlying impulsive noises.La información de navegación es un elemental fundamental para el funcionamiento de sistemas
de transporte inteligentes y plataformas robóticas. Entre las tecnologías existentes, los
Sistemas Globales de Navegación por Satélite (GNSS) se han consolidado como la piedra
angular para la navegación en exteriores, dando acceso a localización y sincronización temporal
a una escala global, irrespectivamente de la condición meteorológica. GNSS es el término
genérico que define una constelación de satélites que transmiten señales de radio, usadas
primordinalmente para proporcionar información de distancia. Por lo tanto, la operatibilidad y
funcionamiento de los futuros sistemas autónomos pende de nuestra capacidad para explotar
GNSS y estimar soluciones de navegación robustas y precisas.
Las señales GNSS permiten dos tipos de observaciones de alcance: –pseudorangos de
código, que miden el tiempo transcurrido entre la emisión de las señales en los satélites y su
acquisición en la tierra por parte de un receptor; –pseudorangos de fase de portadora, que
miden la fase de la onda sinusoide que portan dichas señales y el número acumulado de ciclos
completos. Los pseudorangos de código proporcionan una medida inequívoca de la distancia
entre los satélites y el receptor, con una precisión de decímetros cuando no se tienen en
cuenta los retrasos atmosféricos y los desfases del reloj. En contraposición, las observaciones
de la portadora son super precisas, alcanzando el milímetro de exactidud, a expensas de ser
ambiguas por un número entero y desconocido de ciclos. Por ende, el alcanzar la máxima
precisión con GNSS queda condicionado al uso de las medidas de fase de la portadora, lo
cual implica unos problemas de estimación de elevada complejidad.
Esta tesis versa sobre la teoría de estimación relacionada con la provisión de navegación
precisa basada en la fase de la portadora, especialmente para vehículos que transitan escenarios
donde las señales no se propagan fácilmente, como es el caso de las ciudades. Para ello,
primero se aborda la máxima efectividad del problema de localización, proponiendo cotas
inferiores para el procesamiento de la señal en el receptor y para el problema de estimación
mixto (es decir, cuando las incógnitas pertenecen al espacio de números reales y enteros). En
segundo lugar, se consideran las configuraciones multiantena para el cálculo de la orientación de un vehículo, presentando un nuevo modelo para la estimación conjunta de posición y
rumbo, y proponiendo estimadores deterministas y recursivos basados en la teoría de Lie. Por
último, se explora el marco de la estadística robusta para proporcionar nuevas soluciones de
navegación precisa, capaces de hacer frente a los ruidos atípicos.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: José Manuel Molina López.- Secretario: Giorgi Gabriele.- Vocal: Fabio Dovi
Signal processing techniques for GNSS anti-spoofing algorithms
The Global Navigation Satellite Systems (GNSS) usage is growing at a very high
rate, and more applications are relying on GNSS for correct functioning. With the
introduction of new GNSSs, like the European Galileo and the Chinese Beidou, in
addition to the existing ones, the United States Global Positioning System (GPS)
and the Russian GLONASS, the applications, accuracy of the position and usage of
the signals are increasing by the day.
Given that GNSS signals are received with very low power, they are prone to
interference events that may reduce the usage or decrease the accuracy. From these
interference, the spoofing attack is the one that has drawn major concerns in the
GNSS community. A spoofing attack consist on the transmission of GNSS-like
signals, with the goal of taking control of the receiver and make it compute an
erroneous position and time solution.
In the thesis, we focus on the design and validation of different signal processing
techniques, that aim at detection and mitigation of the spoofing attack effects. These
are standalone techniques, working at the receiver’s level and providing discrimination
of spoofing events without the need of external hardware or communication
links. Four different techniques are explored, each of them with its unique sets of
advantages and disadvantages, and a unique approach to spoofing detection. For
these techniques, a spoofing detection algorithm is designed and implemented, and
its capabilities are validated by means of a set of datasets containing spoofing signals.
The thesis focuses on two different aspects of the techniques, divided as per detection
and mitigation capabilities. Both detection techniques are complementary, their joint
use is explored and experimental results are shown that demonstrate the advantages.
In addition, each mitigation technique is analyzed separately as they require
specialized receiver architecture in order to achieve spoofing detection and mitigation.
These techniques are able to decrease the effects of the spoofing attacks, to the point
of removing the spoofing signal from the receiver and compute navigation solutions
that are not controlled by the spoofer and lead in more accurate end results.
The main contributions of this thesis are: the description of a multidimensional
ratio metric test for distinction between spoofing and multipath effects; the introduction
of a cross-check between automatic gain control measurements and the
carrier to noise density ratio, for distinction between spoofing attacks and other
interference events; the description of a novel signal processing method for detection
and mitigation of spoofing effects, based on the use of linear regression algorithms;
and the description of a spoofing detection algorithm based on a feedback tracking
architecture
3D Localization and Tracking Methods for Multi-Platform Radar Networks
Multi-platform radar networks (MPRNs) are an emerging sensing technology due
to their ability to provide improved surveillance capabilities over plain
monostatic and bistatic systems. The design of advanced detection,
localization, and tracking algorithms for efficient fusion of information
obtained through multiple receivers has attracted much attention. However,
considerable challenges remain. This article provides an overview on recent
unconstrained and constrained localization techniques as well as multitarget
tracking (MTT) algorithms tailored to MPRNs. In particular, two data-processing
methods are illustrated and explored in detail, one aimed at accomplishing
localization tasks the other tracking functions. As to the former, assuming a
MPRN with one transmitter and multiple receivers, the angular and range
constrained estimator (ARCE) algorithm capitalizes on the knowledge of the
transmitter antenna beamwidth. As to the latter, the scalable sum-product
algorithm (SPA) based MTT technique is presented. Additionally, a solution to
combine ARCE and SPA-based MTT is investigated in order to boost the accuracy
of the overall surveillance system. Simulated experiments show the benefit of
the combined algorithm in comparison with the conventional baseline SPA-based
MTT and the stand-alone ARCE localization, in a 3D sensing scenario
Proceedings of the Mobile Satellite Conference
A satellite-based mobile communications system provides voice and data communications to mobile users over a vast geographic area. The technical and service characteristics of mobile satellite systems (MSSs) are presented and form an in-depth view of the current MSS status at the system and subsystem levels. Major emphasis is placed on developments, current and future, in the following critical MSS technology areas: vehicle antennas, networking, modulation and coding, speech compression, channel characterization, space segment technology and MSS experiments. Also, the mobile satellite communications needs of government agencies are addressed, as is the MSS potential to fulfill them
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