Joint CKF-PHD Filter and Map Fusion for 5G Multi-cell SLAM

5G is expected to enable simultaneous vehicle localization and environment mapping (SLAM). Furthermore, vehicular networks will be covered with 5G small cells, wherein the map information is collected at each base station (BS) and then fused so as to promote the overall performance of SLAM. In 5G multi-cell SLAM, there are challenges such as the unknown number of targets, uncertainty regarding the association between the targets and the measurements, unknown types of targets, as well as map management among BSs. To address those challenges, we propose a new method for 5G multi-cell SLAM which comprises a joint cubature Kalman filter and multi-model probability hypothesis density, and a map fusion routine. Simulation results demonstrate that the proposed method solves the aforementioned challenges and also improves vehicle state and map estimates

5G mmWave Cooperative Positioning and Mapping using Multi-Model PHD Filter and Map Fusion

5G millimeter wave (mmWave) signals can enable accurate positioning in vehicular networks when the base station and vehicles are equipped with large antenna arrays. However, radio-based positioning suffers from multipath signals generated by different types of objects in the physical environment. Multipath can be turned into a benefit, by building up a radio map (comprising the number of objects, object type, and object state) and using this map to exploit all available signal paths for positioning. We propose a new method for cooperative vehicle positioning and mapping of the radio environment, comprising a multiple-model probability hypothesis density filter and a map fusion routine, which is able to consider different types of objects and different fields of views. Simulation results demonstrate the performance of the proposed method.Comment: This work has been accepted in the IEEE Transactions on Wireless Communication

5G mmWave Cooperative Positioning and Mapping Using Multi-Model PHD Filter and Map Fusion

5G millimeter wave (mmWave) signals can enable accurate positioning in vehicular networks when the base station and vehicles are equipped with large antenna arrays. However, radio-based positioning suffers from multipath signals generated by different types of objects in the physical environment. Multipath can be turned into a benefit, by building up a radio map (comprising the number of objects, object type, and object state) and using this map to exploit all available signal paths for positioning. We propose a new method for cooperative vehicle positioning and mapping of the radio environment, comprising a multiple-model probability hypothesis density filter and a map fusion routine, which is able to consider different types of objects and different fields of views. Simulation results demonstrate the performance of the proposed method

Estimation and control of multi-object systems with high-fidenlity sensor models: A labelled random finite set approach

Principled and novel multi-object tracking algorithms are proposed, that have the ability to optimally process realistic sensor data, by accommodating complex observational phenomena such as merged measurements and extended targets. Additionally, a sensor control scheme based on a tractable, information theoretic objective is proposed, the goal of which is to optimise tracking performance in multi-object scenarios. The concept of labelled random finite sets is adopted in the development of these new techniques

Nested filtering methods for Bayesian inference in state space models

Mención Internacional en el título de doctorA common feature to many problems in some of the most active fields of science is the need to calibrate (i.e., estimate the parameters) and then forecast the time evolution of high-dimensional dynamical systems using sequentially collected data. In this dissertation we introduce a generalised nested filtering methodology that is structured in (two or more) intertwined layers in order to estimate the static parameters and the dynamic state variables of nonlinear dynamical systems. This methodology is essentially probabilistic. It aims at recursively computing the sequence of posterior probability distributions of the unknown model parameters and its (time-varying) state variables conditional on the available observations. To be specific, in the first layer of the filter we approximate the posterior probability distribution of the static parameters and in the consecutive layers we employ filtering (or data assimilation) techniques to track and predict different conditional probability distributions of the state variables. We have investigated the use of different Monte Carlo-based methods and Gaussian filtering techniques in each of the layers, leading to a wealth of algorithms. In a first approach, we have introduced a nested filtering methodology of two layers that aims at recursively estimating the static parameters and the dynamical state variables of a state space model. This probabilistic scheme uses Monte Carlo-based methods in the first layer of the filter, combined with the use of Gaussian filters in the second layer. Different from the nested particle filter (NPF) of [25], the use of Gaussian filtering techniques in the second layer allows for fast implementations, leading to algorithms that are better suited to high-dimensional systems. As each layer uses different types of methods, we refer to the proposed methodology as nested hybrid filtering. We specifically explore the combination of Monte Carlo and quasi–Monte Carlo approximations in the first layer, including sequential Monte Carlo (SMC) and sequential quasi-Monte Carlo (SQMC), with standard Gaussian filtering methods in the second layer, such as the ensemble Kalman filter (EnKF) and the extended Kalman filter (EKF). However, other algorithms can fit naturally within the framework. Additionally, we prove a general convergence result for a class of procedures that use SMC in the first layer and we show numerical results for a stochastic two-scale Lorenz 96 system, a model commonly used to assess data assimilation (filtering) procedures in Geophysics. We apply and compare different implementations of the methodology to the tracking of the state and the estimation of the fixed parameters. We show estimation and forecasting results, obtained with a desktop computer, for up to 5000 dynamic state variables. As an extension of the nested hybrid filtering methodology, we have introduced a class of schemes that can incorporate deterministic sampling techniques (such as the cubature Kalman filter (CKF) or the unscented Kalman filter (UKF)) in the first layer of the algorithm, instead of the Monte Carlo-based methods employed in the original procedure. As all the methods used in this scheme are Gaussian, we refer to this class of algorithms as nested Gaussian filters. One more time, we reduce the computational cost with the proposed scheme, making the resulting algorithms potentially better-suited for high-dimensional state and parameter spaces. In the numerical results, we describe and implement a specific instance of the new method (a UKF-EKF algorithm) and evaluate its average performance in terms of estimation errors and running times for nonlinear stochastic models. Specifically, we present numerical results for a stochastic Lorenz 63 model using synthetic data, as well as for a stochastic volatility model with real-world data. Finally, we have extended the proposed methodology in order to estimate the static parameters and the dynamical variables of a class of heterogeneous multi-scale state-space models [1]. This scheme combines three or more layers of filters, one inside the other. Each of the layers corresponds to the different time scales that are relevant to the dynamics of this kind of state-space models, allocating the variables with the greatest time scales (the slowest ones) in the outer-most layer and the variables with the smallest time scales (the fastest ones) to the inner-most layer. In particular, we describe a three-layer filter that approximates the posterior probability distribution of the parameters in a first layer of computation, in a second layer we approximate the posterior probability distribution of the slow state variables, and the posterior probability distribution of the fast state variables is approximated in a third layer. To be specific, we describe two possible algorithms that derive from this scheme, combining Monte Carlo methods and Gaussian filters at different layers. The first method uses SMC methods in both first and second layers, together with a bank of UKFs in the third layer (i.e., a SMC-SMC-UKF algorithm). The second method employs a SMC in the first layer, EnKFs at the second layer and introduces the use of a bank of EKFs in the third layer (i.e., a SMC-EnKF-EKF algorithm). We present numerical results for a two-scale stochastic Lorenz 96 model with synthetic data.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Víctor Elvira Arregui.- Secretario: Stefano Cabras.- Vocal: David Luengo Garcí

Multitarget tracking and terrain-aided navigation using square-root consider filters

Filtering is a term used to describe methods that estimate the values of partially observed states, such as the position, velocity, and attitude of a vehicle, using current observations that are corrupted due to various sources, such as measurement noise, transmission dropouts, and spurious information. The study of filtering has been an active focus of research for decades, and the resulting filters have been the cornerstone of many of humankind\u27s greatest technological achievements. However, these achievements are enabled principally by the use of specialized techniques that seek to, in some way, combat the negative impacts that processor roundoff and truncation error have on filtering. Two of these specialized techniques are known as square-root filters and consider filters. The former alleviates the fragility induced from estimating error covariance matrices by, instead, managing a factorized representation of that matrix, known as a square-root factor. The latter chooses to account for the statistical impacts a troublesome system parameter has on the overall state estimate without directly estimating it, and the result is a substantial reduction in numerical sensitivity to errors in that parameter. While both of these techniques have found widespread use in practical application, they have never been unified in a common square-root consider framework. Furthermore, consider filters are historically rooted to standard, vector-valued estimation techniques, and they have yet to be generalized to the emerging, set-valued estimation tools for multitarget tracking. In this dissertation, formulae for the square-root consider filter are derived, and the result is extended to finite set statistics-based multitarget tracking tools. These results are used to propose a terrain-aided navigation concept wherein data regarding a vehicle\u27s environment is used to improve its state estimate, and square-root consider techniques provide the numerical stability necessary for an onboard navigation application. The newly developed square-root consider techniques are shown to be much more stable than standard formulations, and the terrain-aided navigation concept is applied to a lunar landing scenario to illustrate its applicability to navigating in challenging environments --Abstract, page iii

Multi-sensor Suboptimal Fusion Student's tt Filter

A multi-sensor fusion Student's $t$ filter is proposed for time-series recursive estimation in the presence of heavy-tailed process and measurement noises. Driven from an information-theoretic optimization, the approach extends the single sensor Student's $t$ Kalman filter based on the suboptimal arithmetic average (AA) fusion approach. To ensure computationally efficient, closed-form $t$ density recursion, reasonable approximation has been used in both local-sensor filtering and inter-sensor fusion calculation. The overall framework accommodates any Gaussian-oriented fusion approach such as the covariance intersection (CI). Simulation demonstrates the effectiveness of the proposed multi-sensor AA fusion-based $t$ filter in dealing with outliers as compared with the classic Gaussian estimator, and the advantage of the AA fusion in comparison with the CI approach and the augmented measurement fusion.Comment: 8 pages, 8 figure

Fight sample degeneracy and impoverishment in particle filters: A review of intelligent approaches

During the last two decades there has been a growing interest in Particle Filtering (PF). However, PF suffers from two long-standing problems that are referred to as sample degeneracy and impoverishment. We are investigating methods that are particularly efficient at Particle Distribution Optimization (PDO) to fight sample degeneracy and impoverishment, with an emphasis on intelligence choices. These methods benefit from such methods as Markov Chain Monte Carlo methods, Mean-shift algorithms, artificial intelligence algorithms (e.g., Particle Swarm Optimization, Genetic Algorithm and Ant Colony Optimization), machine learning approaches (e.g., clustering, splitting and merging) and their hybrids, forming a coherent standpoint to enhance the particle filter. The working mechanism, interrelationship, pros and cons of these approaches are provided. In addition, approaches that are effective for dealing with high-dimensionality are reviewed. While improving the filter performance in terms of accuracy, robustness and convergence, it is noted that advanced techniques employed in PF often causes additional computational requirement that will in turn sacrifice improvement obtained in real life filtering. This fact, hidden in pure simulations, deserves the attention of the users and designers of new filters