Juan A. Besada

A new positioning technique for RSS-Based localization based on a weighted least squares estimator

In this paper we propose the use of a weighted least squares estimator to calculate the position of a mobile node in RSS-based localization systems for ad hoc networks. This technique outperforms the traditional positioning algorithms in terms of localization accuracy and robustness to inaccuracy in the channel model. The performance of the method is shown both through numerical simulations and through some experiments with real data for a wireless sensor network and a WiFi network.

Automated Aircraft Trajectory Prediction Based on Formal Intent-Related Language Processing

This paper proposes a new paradigm for the formulation of aircraft trajectory computation and prediction processes. Under this paradigm, an aircraft trajectory can be expressed at different levels of detail using a set of novel intent-related formal languages. These languages can be used for describing different aspects of aircraft motion or the resulting predicted trajectory, ranging from high-level restrictions imposed on the trajectory to details on how the aircraft will be operated at any specific instant. After defining the complete hierarchy of these formal languages, the concept of trajectory language processing engine is introduced. Every engine can perform automated modifications over a trajectory specified at different levels or at the same level considering the language hierarchy. Finally, several engine examples are described, and a complete trajectory computation process is built as an engine composed through the smart interconnection of several engines. Examples of executions of these automated processes are also included.

A multitarget tracking video system based on fuzzy and neuro-fuzzy techniques

Automatic surveillance of airport surface is one of the core components of advanced surface movement, guidance, and control systems (A-SMGCS). This function is in charge of the automatic detection, identification, and tracking of all interesting targets (aircraft and relevant ground vehicles) in the airport movement area. This paper presents a novel approach for object tracking based on sequences of video images. A fuzzy system has been developed to ponder update decisions both for the trajectories and shapes estimated for targets from the image regions extracted in the images. The advantages of this approach are robustness, flexibility in the design to adapt to different situations, and efficiency for operation in real time, avoiding combinatorial enumeration. Results obtained in representative ground operations show the system capabilities to solve complex scenarios and improve tracking accuracy. Finally, an automatic procedure, based on neuro-fuzzy techniques, has been applied in order to obtain a set of rules from representative examples. Validation of learned system shows the capability to learn the suitable tracker decisions.

Multisensor Out of Sequence Data Fusion for Estimating the State of Discrete Control Systems

The fusion center of a complex control system estimates its state with the information provided by different sensors. Physically distributed sensors, communication networks, pre-processing algorithms, multitasking, etc, introduce non-systematic delays in the arrival of information to the fusion center, making the information available out-of-sequence (OOS). For real-time control systems, the state has to be efficiently estimated with all the information received so far. So, several solutions of the OOS problem for dynamic multiple-input multiple-output (MIMO) discrete control systems traditionally solved by the Kalman filter (KF) have been proposed recently. This paper presents two new streamlined algorithms for the linear and non-linear case. IFAsyn, the linear algorithm, is equivalent to other optimal solutions but more general, efficient and easy to implement. EIFAsyn, the nonlinear one, is a new solution of the OOS problem in the extended KF (EKF) framework.

Analysis of tracking methods for wireless indoor localization

In this paper we perform a comparative analysis of several localization and tracking methods based on WIFI networks. We describe the signal environment basis of the position observations, and we discuss on training needs, localization computational needs, accuracy, stability, etc. The study is based on simulations of the signal fading effects, and on measurements taken in an experimental deployment

Multisensor fusion for linear control systems with asynchronous, Out-Of-Sequence and erroneous data

This paper presents a set of new centralized algorithms for estimating the state of linear dynamic Multiple-Input Multiple-Output (MIMO) control systems with asynchronous, non-systematically delayed and corrupted measurements provided by a set of sensors. The delays, which make the data available Out-Of-Sequence (OOS), appear when using physically distributed sensors, communication networks and pre-processing algorithms. The potentially corrupted measurements can be generated by malfunctioning sensors or communication errors. Our algorithms, designed to work with real-time control systems, handle these problems with a streamlined memory and computational efficient reorganization of the basic operations of the Kalman and Information Filters (KF & IF). The two versions designed to deal only with valid measurements are optimal solutions of the OOS problem, while the other two remaining are suboptimal algorithms able to handle corrupted data.

Aircraft identification integrated into an airport surface surveillance video system

Abstract.A video aircraft identification algorithm, based on tail number recognition, is proposed as part of a global airport surveillance video system. The recognition procedure searches and detects the presence of the tail number in the image and then recognizes the tail number using pattern matching techniques. The identification system has been designed to deal with airport real images, taking into account letter size differences and potential deformations. Finally, the aircraft identification system calculates the joint probability of each tail number in the airport database. The tail number maximizing the joint probability is selected. Results show that the identification procedure achieves a robust identification using the database.

A novel system for object pose estimation using fused vision and inertial data

We present a novel system to object pose estimation by fusing vision and inertial data.Different algorithms for fusing data from inertial sensors and monocular or stereo vision data are described and compared.The system error propagation property is analyzed.The performance of the proposed system is assessed by simulation and experimental data.The system can achieve accurate, fast and low-cost 6-DoF pose estimation. Six-degree-of-freedom (6-DoF) pose estimation is of fundamental importance to many applications, such as robotics, indoor tracking and Augmented Reality. Although a number of pose estimation solutions have been proposed, it remains a critical challenge to provide a low-cost, real-time, accurate and easy-to-deploy solution. Addressing this issue, this paper describes a multisensor system for accurate pose estimation that relies on low-cost technologies, in particular on a combination of webcams, inertial sensors and a printable colored fiducial. With the aid of inertial sensors, the system can estimate full pose both with monocular and stereo vision. The system error propagation is analyzed and validated by simulations and experimental tests. Our error analysis and experimental data demonstrate that the proposed system has great potential in practical applications, as it achieves high accuracy (in the order of centimeters for the position estimation and few degrees for the orientation estimation) using the mentioned low-cost sensors, while satisfying tight real-time requirements.

Trajectory reconstruction techniques for evaluation of ATC systems

This paper is focused on trajectory reconstruction techniques for evaluating ATC systems, using real data of recorded opportunity traffic. We analyze different alternatives for this problem, from traditional interpolation approaches based on curve fitting to our proposed schemes based on modeling regular motion patterns with optimal smoothers. The extraction of trajectory features such as motion type (or mode of flight), maneuvers profile, geometric parameters, etc., allows a more accurate computation of the curve and the detailed evaluation of the data processors used in the ATC centre. Different alternatives will be compared with some performance results obtained with simulated and real data sets.

Model-based trajectory reconstruction using IMM smoothing and motion pattern identification

This work addresses off-line accurate trajectory reconstruction for air traffic control. We propose the use of specific dynamic models after identification of regular motion patterns. Datasets recorded from opportunity traffic are first segmented in motion segments, based on the mode probabilities of an IMM filter. Then, reconstruction is applied with an optimal smoothing filter operating forward and backward. The parameters describing the specific modes are estimated and then used as external input for smoothing filters. The performance of this approach is compared with a method based on interpolation B-splines. Comparative results on simulated and real data are discussed at the end.