Fingerprinting-based indoor localization using interpolated preprocessed csi phases and bayesian tracking

Indoor positioning using Wi-Fi signals is an economic technique. Its drawback is that multipath propagation distorts these signals, leading to an inaccurate localization. An approach to improve the positioning accuracy consists of using fingerprints based on channel state information (CSI). Following this line, we propose a new positioning method which consists of three stages. In the first stage, which is run during initialization, we build a model for the fingerprints of the environment in which we do localization. This model permits obtaining a precise interpolation of fingerprints at positions where a fingerprint measurement is not available. In the second stage, we use this model to obtain a preliminary position estimate based only on the fingerprint measured at the receiver’s location. Finally, in the third stage, we combine this preliminary estimation with the dynamical model of the receiver’s motion to obtain the final estimation. We compare the localization accuracy of the proposed method with other rival methods in two scenarios, namely, when fingerprints used for localization are similar to those used for initialization, and when they differ due to alterations in the environment. Our experiments show that the proposed method outperforms its rivals in both scenarios.Fil: Wang, Wenxu. Guandong University Of Technology; ChinaFil: Marelli, Damian Edgardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; ArgentinaFil: Fu, Minyue. Universidad de Newcastle; Australi

Dynamic indoor localization using maximum likelihood particle filtering

A popular approach for solving the indoor dynamic localization problem based on WiFi measurements consists of using particle filtering. However, a drawback of this approach is that a very large number of particles are needed to achieve accurate results in real environments. The reason for this drawback is that, in this particular application, classical particle filtering wastes many unnecessary particles. To remedy this, we propose a novel particle filtering method which we call maximum likelihood particle filter (MLPF). The essential idea consists of combining the particle prediction and update steps into a single one in which all particles are efficiently used. This drastically reduces the number of particles, leading to numerically feasible algorithms with high accuracy. We provide experimental results, using real data, confirming our claim.Fil: Wang, Wenxu. Guangdong University of Technology; ChinaFil: Marelli, Damian Edgardo. Guangdong University of Technology; China. Centro Científico Nacional e Internacional Francés Argentino de Ciencias de la Información y Sistemas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Fu, Minyue. Universidad de Newcastle; Australia. Guangdong University of Technology; Chin

A subband approach to channel estimation and equalization for DMT and OFDM systems

Cyclic prefix (CP) is commonly used for channel equalization of discrete multitone (DMT) and orthogonal frequency-division multiplexing (OFDM) systems. This is often done in conjunction with a time-domain equalizer (TEQ) for reducing the capacity overhead caused by the CP. However, the use of TEQ greatly increases the computational cost, and is unable to eliminate the need for the CP. In this paper, we propose a subband approach to channel estimation and channel equalization for DMT and OFDM systems. This approach involves splitting the received signals into a number of frequency bands (called subbands), and estimating a constant parameter in each subband. The subband approach is conceptually simple, requires no CP, is much more numerically efficient than the TEQ method, and gives compatible or better estimation errors than the CP-based methods

Asymptotic properties of statistical estimators using multivariate Chi-squared measurements

This paper studies the problem of estimating a parameter vector from measurements having a multivariate chi-squared distribution. Maximum likelihood estimation in this setting is unfeasible because the multivariate chi-squared distribution has no closed form expression. The typical approach to go around this consists in considering a sub-optimal solution by replacing the chi-squared distribution with a normal one. We investigate the theoretical properties of this approximation as the number of measurements approach infinity. More precisely, we show that this approximation is strongly consistency, asymptotically normal and asymptotically efficient. We consider a source localization problem as a case study.Fil: Marelli, Damian Edgardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina. Guandong University of Technology; ChinaFil: Fu, Minyue. Universidad de Newcastle; Australia. Guandong University of Technology; Chin

Distributed Kalman estimation with decoupled local filters

We study a distributed Kalman filtering problem in which a number of nodes cooperate without central coordination to estimate a common state based on local measurements and data received from neighbors. This is typically done by running a local filter at each node using information obtained through some procedure for fusing data across the network. A common problem with existing methods is that the outcome of local filters at each time step depends on the data fused at the previous step. We propose an alternative approach to eliminate this error propagation. The proposed local filters are guaranteed to be stable under some mild conditions on certain global structural data, and their fusion yields the centralized Kalman estimate. The main feature of the new approach is that fusion errors introduced at a given time step do not carry over to subsequent steps. This offers advantages in many situations including when a global estimate is only needed at a rate slower than that of measurements or when there are network interruptions. If the global structural data can be fused correctly asymptotically, the stability of local filters is equivalent to that of the centralized Kalman filter. Otherwise, we provide conditions to guarantee stability and bound the resulting estimation error. Numerical experiments are given to show the advantage of our method over other existing alternatives.Fil: Marelli, Damian Edgardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Guangdong University Of Technology; ChinaFil: Sui, Tianju. Dalian University Of Technology; ChinaFil: Fu, Minyue. Universidad de Newcastle; Australi

Multiple-Vehicle Localization Using Maximum Likelihood Kalman Filtering and Ultra-Wideband Signals

In this article we study the problem of localizing a fleet of vehicles in an indoor environment using ultra-wideband (UWB) signals. This is typically done by placing a number of UWB anchors with respect to which vehicles measure their distances. The localization performance is usually poor in the vertical axis, due to the fact that anchors are often placed at similar heights. To improve accuracy, we study the use of inter-vehicle distance measurements. These measurements introduce a technical challenge, as this requires the joint estimation of positions of all vehicles, and currently available methods become numerically complex. To go around this, we use a recently proposed technique called maximum likelihood Kalman filtering (MLKF). We present experiments using real data, showing how the addition of inter-vehicle measurements improves the localization accuracy by about 60%. Experiments also show that the MLKF achieves a localization error similar to the best among available methods, while requiring only about 20% of computational time.Fil: Wang, Wenxu. Guandong University Of Technology; ChinaFil: Marelli, Damian Edgardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; ArgentinaFil: Fu, Minyue. Universidad de Newcastle; Australi

Distributed Kalman filter in a network of linear systems

This paper is concerned with the problem of distributed Kalman filtering in a network of interconnected subsystems with distributed control protocols. We consider networks, which can be either homogeneous or heterogeneous, of linear time-invariant subsystems, given in the state-space form. We propose a distributed Kalman filtering scheme for this setup. The proposed method provides, at each node, an estimation of the state parameter, only based on locally available measurements and those from the neighbor nodes. The special feature of this method is that it exploits the particular structure of the considered network to obtain an estimate using only one prediction/update step at each time step. We show that the estimate produced by the proposed method asymptotically approaches that of the centralized Kalman filter, i.e., the optimal one with global knowledge of all network parameters, and we are able to bound the convergence rate. Moreover, if the initial states of all subsystems are mutually uncorrelated, the estimates of these two schemes are identical at each time step.Fil: Marelli, Damian Edgardo. Zhejiang University; China. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; ArgentinaFil: Zamani, Mohsen. Universidad de Newcastle; AustraliaFil: Fu, Minyue. Zhejiang University; China. Universidad de Newcastle; AustraliaFil: Ninness, Brett. Universidad de Newcastle; Australi

Accuracy analysis for distributed weighted least-squares estimation in finite steps and loopy networks

Distributed parameter estimation for large-scale systems is an active research problem. The goal is to derive a distributed algorithm in which each agent obtains a local estimate of its own subset of the global parameter vector, based on local measurements as well as information received from its neighbors. A recent algorithm has been proposed, which yields the optimal solution (i.e., the one that would be obtained using a centralized method) in finite time, provided the communication network forms an acyclic graph. If instead, the graph is cyclic, the only available alternative algorithm, which is based on iterative matrix inversion, achieving the optimal solution, does so asymptotically. However, it is also known that, in the cyclic case, the algorithm designed for acyclic graphs produces a solution which, although non optimal, is highly accurate. In this paper we do a theoretical study of the accuracy of this algorithm, in communication networks forming cyclic graphs. To this end, we provide bounds for the sub-optimality of the estimation error and the estimation error covariance, for a class of systems whose topological sparsity and signal-to-noise ratio satisfy certain condition. Our results show that, at each node, the accuracy improves exponentially with the so-called loop-free depth. Also, although the algorithm no longer converges in finite time in the case of cyclic graphs, simulation results show that the convergence is significantly faster than that of methods based on iterative matrix inversion. Our results suggest that, depending on the loop-free depth, the studied algorithm may be the preferred option even in applications with cyclic communication graphs.Fil: Sui, Tianju. Dalian University of Technology; ChinaFil: Marelli, Damian Edgardo. Guandong University of Technology; China. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; ArgentinaFil: Fu, Minyue. Guandong University of Technology; China. Universidad de Newcastle; AustraliaFil: Lu, Renquan. Guandong University of Technology; Chin

Distributed Target Tracking Using Maximum Likelihood Kalman Filter with Non-Linear Measurements

We propose a distributed method for tracking a target with linear dynamics and non-linear measurements acquired by a number of sensors. The proposed method is based on a Bayesian tracking technique called maximum likelihood Kalman filter (MLKF), which is known to be asymptotically optimal, in the mean squared sense, as the number of sensors becomes large. This method requires, at each time step, the solution of a maximum likelihood (ML) estimation problem as well as the Hessian matrix of the likelihood function at the optimal. In order to obtain a distributed method, we compute the ML estimate using a recently proposed fully distributed optimization method, which yields the required Hessian matrix as a byproduct of the optimization procedure. We call the algorithm so obtained the distributed MLKF (DMLKF). Numerical simulation results show that DMLKF largely outperforms other available distributed tracking methods, in terms of tracking accuracy, and that it asymptotically approximates the optimal Bayesian tracking solution, as the number of sensors and inter-node information fusion iterations increase.Fil: Huang, Zenghong. Guangdong University of Technology; ChinaFil: Marelli, Damian Edgardo. Guangdong University of Technology; China. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; ArgentinaFil: Xu, Yong. Guangdong University of Technology; ChinaFil: Fu, Minyue. Universidad de Newcastle; Australi

LQG Differential Stackelberg Game under Nested Observation Information Patterns

We investigate the linear quadratic Gaussian Stackelberg game under a class of nested observation information patterns. The follower uses its observation data to design its strategy, whereas the leader implements its strategy using global observation data. We show that the solution requires solving a new type of forward-backward stochastic differential equation, whose drift components contain two conditional expectation terms associated to the adjoint variables. We then propose a method to find the functional relations between each adjoint pair, i.e., each pair formed by an adjoint variable and the conditional expectation of its associated state. The proposed method follows a layered pattern. More precisely, in the inner layer, we seek the functional relation for the adjoint pair under the sigma-sub-algebra generated by follower's observation information; and in the outer layer, we look for the functional relation for the adjoint pair under the sigma-sub-algebra generated by leader's observation information. Our result shows that the optimal open-loop solution admits an explicit feedback type representation.Fil: Li, Zhipeng. Guangdong University Of Technology; ChinaFil: Marelli, Damian Edgardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; ArgentinaFil: Fu, Minyue. Universidad de Newcastle; AustraliaFil: Zhang, Huanshui. Shandong University; Chin