Pau Closas

Indoor Tracking: Theory, Methods, and Technologies

In the last decade, the research on and the technology for outdoor tracking have seen an explosion of advances. It is expected that in the near future, we will witness similar trends for indoor scenarios where people spend more than 70% of their lives. The rationale for this is that there is a need for reliable and high-definition real-time tracking systems that have the ability to operate in indoor environments, thus complementing those based on satellite technologies, such as the Global Positioning System (GPS). The indoor environments are very challenging, and as a result, a large variety of technologies have been proposed for coping with them, but no legacy solution has emerged. This paper presents a survey on indoor wireless tracking of mobile nodes from a signal processing perspective. It can be argued that the indoor tracking problem is more challenging than the problem on indoor localization. The reason is simple: From a set of measurements, one has to estimate not one location but a series of correlated locations of a mobile node. The paper illustrates the theory, the main tools, and the most promising technologies for indoor tracking. New directions of research are also discussed.

A Bayesian Approach to Multipath Mitigation in GNSS Receivers

Multipath is known to be one of the most dominant sources of accuracy degradation in satellite-based navigation systems. Multipath may cause biased position estimates that could jeopardize high-precision applications. This paper considers the problem of tracking the time-variant synchronization parameters of both the line-of-sight signal (LOSS) and its multipath replicas. In particular, the proposed algorithm tracks time-delays, amplitudes, phases and proposes a procedure to extract Doppler shifts from complex amplitudes. However, the interest is focused on LOSS time-delay estimates, since those provide the means to compute users position. The undertaken Bayesian approach is implemented by a particle filter. The selection of the importance density function, from which particles are generated, is performed using a Gaussian approximation of the posterior function. This selection provides a particle generating function close to the optimal, which yields to an efficient usage of particles. The complex-linear part of the model, i.e., complex amplitudes, is tackled by a Rao-Blackwellization procedure that implements a complex Kalman filter for each generated particle, thus reducing the computational load. Computer simulation results are compared to other Bayesian filtering alternatives (namely, the extended Kalman filter, the unscented Kalman filter and the sequential importance resampling algorithms) and the posterior Cramer-Rao bound.

CramÉr–Rao Bound Analysis of Positioning Approaches in GNSS Receivers

Recently, direct position estimation (DPE) has arisen as a potential approach to deal with the positioning problem in global navigation satellite system receivers. The conventional navigation solution is obtained in two steps: synchronization parameters are estimated and then a trilateration procedure is in charge of computing users position, based on those parameters. In contrast, DPE estimates receivers position directly from digitized signal. DPE was seen to provide GNSS receivers with appealing capabilities, such as multipath mitigation. However, a theoretical bound for those estimates is still missing and the answer to ldquohow better can DPE perform compared to the conventional approach?rdquo has not been addressed in the literature. Aiming at clarifying those issues, this paper presents the derivation of the CramEacuter-Rao bound (CRB) of position for both conventional and DPE approaches. We present the derivation for a multiantenna receiver as a general case. In addition, a number of realistic scenarios are tested in order to compare the theoretical performance bounds of both alternatives and the actual root mean squared error performance of the corresponding maximum likelihood estimator.

Antenna Array Based GNSS Signal Acquisition for Interference Mitigation

This work addresses the signal acquisition problem using an array of antennas in the general framework of Global Navigation Satellite Systems (GNSS) receivers. We propose a statistical approach, using the Neyman-Pearson (NP) detection theory and the generalized likelihood ratio test (GLRT), to obtain a new detector which is able to mitigate temporally uncorrelated interferences even if the array is unstructured and moderately uncalibrated. The key statistical feature is the assumption of an arbitrary and unknown covariance noise matrix, which attempts to capture the statistical behavior of the interferences and of other nondesirable signals, while exploiting the spatial dimension provided by antenna arrays. Closed-form expressions for detection and false alarm probabilities are provided. The performance and interference rejection capability are modeled and compared with their theoretical bound. Furthermore the proposed detector is analyzed under realistic conditions, which accounts for the presence of errors in the covariance matrix estimation, the residual Doppler and delay errors, and the signal quantization effects. The theoretical results are supported by Monte Carlo simulations.

Multiple Quadrature Kalman Filtering

Bayesian filtering is a statistical approach that naturally appears in many signal processing problems. Ranging from Kalman filter to particle filters, there is a plethora of alternatives depending on model assumptions. With the exception of very few tractable cases, one has to resort to suboptimal methods due to the inability to analytically compute the Bayesian recursion in general dynamical systems. This is why it has attracted the attention of many researchers in order to develop efficient algorithms to implement it. We focus our interest into a recently developed algorithm known as the Quadrature Kalman filter (QKF). Under the Gaussian assumption, the QKF can tackle arbitrary nonlinearities by resorting to the Gauss-Hermite quadrature rules. However, its complexity increases exponentially with the state-space dimension. In this paper we study a complexity reduction technique for the QKF based on the partitioning of the state-space, referred to as the Multiple QKF. We prove that partitioning schemes can effectively be used to reduce the curse of dimensionality in the QKF. Simulation results are also provided to show that a nearly-optimal performance can be attained, while drastically reducing the computational complexity with respect to state-of-the-art algorithms that are able to deal with such nonlinear filtering problems.

A game theoretical algorithm for joint power and topology control in distributed WSN

In this paper, the issue of network topology control in wireless networks using a fully distributed algorithm is considered. Whereas the proposed distributed algorithm is designed applying game theory concepts to design a non-cooperative game, network connectivity is guaranteed based on asymptotic results of network connectivity. Simulations show that for a relatively low node density, the probability that the proposed algorithm leads to a connected network is close to one.

Bayesian nonlinear filters for Direct Position Estimation

The use of a Direct Position Estimation approach has recently deserved some attention in the satellite-based navigation topic. In this paper, the core idea is to merge a motion model based on the observations of an Inertial Measurement Unit, accounting for possible biased measures, with a signal model parameterized by the position of the receiver. Indeed, this position is to be estimated. Bayesian nonlinear filtering theory is reviewed in the paper. Particularly, we focus our attention on the study of particle filtering and square-root derivative-free algorithms based on the Gaussian assumption and approximation rules for numerical integration, namely the Gauss-Hermite quadrature rule or the third-degree spherical-radial cubature rule. These algorithms exhibit a dramatic improvement and better numerical stability than classical Kalman filter-like methods, for example the extended Kalman filter or the unscented Kalman filter. The paper presents an analysis of the computational complexity of each algorithm and a performance comparison using computer simulations under a realistic scenario.

Improving Accuracy by Iterated Multiple Particle Filtering

This paper analyzes and validates an enhanced implementation of the multiple particle filter that improves its accuracy when applied to high dimensional problems. The algorithm combines the divide et impera philosophy of the multiple particle filter, which avoids the collapse of traditional particle filters, with game theory strategies that provide with a powerful tool to improve the performance. The problem of multiple target tracking with received signal strength measurements is addressed and the results show remarkable improvement over both standard particle filtering and multiple particle filtering.

Robust GNSS Receivers by Array Signal Processing: Theory and Implementation

One of the main vulnerabilities of GNSS receivers is their exposure to intentional or unintentional jamming signals, which could even cause service unavailability. Several alternatives to counteract these effects were proposed in the literature, being the most promising those based on multiple antenna architectures. This is specially the case for high-grade receivers used in applications requiring reliability and robustness. This article provides an overview of the possible receiver architectures encompassing antenna arrays and the associated signal processing techniques. Emphasis is also put on the most typical implementation issues found when dealing with such technology. A thorough survey is complemented with a set of experiments, including real data processing by a working prototype, which exemplifies the above ideas.

Bayesian filtering for indoor localization and tracking in wireless sensor networks

In this article, we investigate experimentally the suitability of several Bayesian filtering techniques for the problem of tracking a moving device by a set of wireless sensor nodes in indoor environments. In particular, we consider a setup where a robot was equipped with an ultra-wideband (UWB) node emitting ranging signals; this information was captured by a network of static UWB sensor nodes that were in charge of range computation. With the latter, we ran, analyzed, and compared filtering techniques to track the robot. Namely, we considered methods falling into two families: Gaussian filters and particle filters. Results shown in the article are with real data and correspond to an experimental setup where the wireless sensor network was deployed. Additionally, statistical analysis of the real data is provided, reinforcing the idea that in this kind of ranging measurements, the Gaussian noise assumption does not hold. The article also highlights the robustness of a particular filter, namely the cost-reference particle filter, to model inaccuracies which are typical in any practical filtering algorithm.