David Gualda

Extensive Ultrasonic Local Positioning System for navigating with mobile robots

This paper presents an extensive local ultrasonic positioning system for mobile robots (MRs) based on the fusion of the information from the MR internal odometer sensor and the measurements of position obtained with a set of external ultrasonic beacons that form an Ultrasonic Local Positioning System (ULPS). The extensive ULPS is formed using several single ULPS, each one of them covering a particular area of the total working area. In each single ULPS, five ultrasonic beacons are used that emit simultaneously and are detected by an ultrasonic receiver on board the MR. Direct Sequence Code Division Multiple Access (DS-CDMA) techniques have been used to avoid interferences between them. When the MR navigates on the working area, it continuously obtains its relative position thanks to the internal odometer. The cumulative errors of the odometer are corrected when the robot is on a place where the positioning data obtained with any of the single ULPS are available. The fusion of information (from odometer and ULPS) is made using an H-Infinite filter. The extensive ULPS has been built and tested experimentally.

Locally-referenced ultrasonic--LPS for localization and navigation.

This paper presents a flexible deployment of ultrasonic position sensors and a novel positioning algorithm suitable for the navigation of mobile robots (MRs) in extensive indoor environments. Our proposal uses several independently-referenced local positioning systems (LPS), which means that each one of them operates within its own local reference system. In a typical layout, an indoor extensive area can be covered using just a reduced set of globally-referenced LPS (GRLPS), whose beacon positions are known to the global reference system, while the rest of the space can be covered using locally-referenced LPSs (LRLPS) that can be distributed arbitrarily. The number of LRLPS and their position can be also changed at any moment. The algorithm is composed of several Bayesian filters running in parallel, so that when an MR is under the GRLPS coverage area, its position is updated by a global filter, whereas when the MR is inside the LRLPS area, its position is updated using position increments within a local filter. The navigation algorithm has been tested by simulation and with actual data obtained using a real set of ultrasonic LPSs.

Indoor location system based on ZigBee devices and Metric Description Graphs

This paper proposes a way to improve the position estimates in an indoor location systems (ILS) based on readings from the power levels (RSSI) of an ad hoc ZigBee network. A mobile object, also equipped with a ZigBee device, can be located, positioned, and tracked by the system. The initial estimate of the mobile node position is extracted from IC devices from Texas Instruments, equipped with a specific hardware module for this function called Location Engine; it computes its position from RSSI readings of the signals coming from a set of reference beacons. Positioning of the blind nodes is enhanced by a post-filtering of the initial estimates by adjusting them into a Metric Description Graph (MDG) of the building, which includes information on distances and connectivity among the various enclosures of the coverage area of the ILS. The system has been experimentally verified in localization tasks of pedestrians in indoor environments.

Android application for indoor positioning of mobile devices using ultrasonic signals

In this paper we present an Android application, called LOCATE-US, that allows accurate indoor positioning of mobile devices by processing the ultrasonic signals coming from a local positioning system (LPS). The LPS operates around 41KHz, and an external hardware based on an ultrasonic microphone digitizes the incoming signals and send them to the mobile device, where they are processed by means of the proposed application. This allows the use of CDMA techniques and overcomes most of the problems to be faced at the 20-22kHz range available in current smartphones, which include audible artifacts and poor resolution. The proposed software allows each mobile device in the environment to compute its own position by means of hyperbolic trilateration and represents the trajectory in the device screen, for future Location Based Services applications. Experimental results show that the proposed application achieves centimeter accuracy and can execute all the data processing for the position estimation in a time less than 0.5s.

A robust UWB indoor positioning system for highly complex environments

Ultra-Wideband (UWB) has a high interest in research and industry for accurate indoor positioning. This technology comprises signals with a bandwidth of at least 500 MHz at a power decay of -10dB. This large bandwidth conferes the capability of resolving multipath, penetrating through obstacles and accurate ranging. However, NLOS (Non-Line-Of-Sight) conditions produced by obstacles in indoor environments severely degrade performance, as they introduce a positive bias in ranging estimation. In this paper we present a robust UWB indoor positioning which is able to accurately operate in a highly complex indoor scenario, where NLOS condition is predominant. For that purpose, the system uses a NLOS detection algorithm based on the skewness of the estimated channel impulse response and it mitigates NLOS by using an Extended Kalman Filter. The use of this detection/mitigation algorithm shows an improvement of the RMSE in positioning about 67%.

Ultrasonic signal acquisition module for smartphone indoor positioning

Smartphone capabilities can significantly enhance mobile and context-aware applications that depend on location information. Whereas outdoor Location Based Services (LBS) are highly developed thanks to GPS, indoor LBS still demand a positioning technology that provides accurate location data. In this paper, an ultrasonic signal acquisition module for fine-grained indoor positioning of portable devices, such as smartphones or tablets, is presented. It is based on a microcontroller that digitizes the signals coming from a set of ultrasonic beacons placed on the ceiling and transfers the data acquired through Bluetooth Wireless Technology to the smartphone, for their consequent processing. All ultrasonic beacons emit simultaneously, each one a different Kasami code which is BPSK modulated with a carrier frequency of 40kHz. At the reception, the proposed module, that can coexist with an unlimited number of receivers, digitizes the analog signal captured by an electret microphone and sends it to the smartphone. Then, the smartphone carries out the correlation of the received signal with the emitted codes and obtains its absolute position by hyperbolic trilateration. Test results show that the proposed system can achieve similar accuracies to conventional ultrasonic based local positioning systems, with the advantage of services of a smartphone.

Ultrasonic LPS adaptation for smartphones

In this paper we present a privacy-oriented Ultrasonic Local Positioning System (U-LPS) allowing users with portable devices, such as smartphones or tablets, to accurately determine their position in indoor environments. Indoor U-LPSs have been widely used to provide location information with centimeter or even sub-centimeter level accuracy at high rates. However, most of these systems demand specific hardware in the reception stage that cannot be used with current smartphones. Hence, the user cannot benefit from the versatility of services that these kind of devices offer. This work presents the design of an external device which translates the high frequency ultrasonic information emitted by a CDMA-based U-LPS, into a low frequency band understandable for the mobile phone by means of an analog multiplier. The output of the multiplier is connected to the smartphone or tablet using the Jack input connector as if it was a microphone. Then, the mobile device processes the low band frequency signals and estimates its position.

Assessing Human Activity in Elderly People Using Non-Intrusive Load Monitoring

The ageing of the population, and their increasing wish of living independently, are motivating the development of welfare and healthcare models. Existing approaches based on the direct heath-monitoring using body sensor networks (BSN) are precise and accurate. Nonetheless, their intrusiveness causes non-acceptance. New approaches seek the indirect monitoring through monitoring activities of daily living (ADLs), which proves to be a suitable solution. ADL monitoring systems use many heterogeneous sensors, are less intrusive, and are less expensive than BSN, however, the deployment and maintenance of wireless sensor networks (WSN) prevent them from a widespread acceptance. In this work, a novel technique to monitor the human activity, based on non-intrusive load monitoring (NILM), is presented. The proposal uses only smart meter data, which leads to minimum intrusiveness and a potential massive deployment at minimal cost. This could be the key to develop sustainable healthcare models for smart homes, capable of complying with the elderly people’ demands. This study also uses the Dempster-Shafer theory to provide a daily score of normality with regard to the regular behavior. This approach has been evaluated using real datasets and, additionally, a benchmarking against a Gaussian mixture model approach is presented.

Event-Based Energy Disaggregation Algorithm for Activity Monitoring From a Single-Point Sensor

The massive deployment of smart meters and other customized meters has motivated the development of nonintrusive load monitoring (NILM) systems. This is the process of disaggregating the total energy consumption in a building into individual electrical loads using a single-point sensor. Most literature is oriented to energy saving. Nevertheless, activity of daily livings monitoring through NILM is recently receiving much interest. This proposal presents an event-based NILM algorithm of high performance for activity monitoring applications. This is divided into two stages: 1) an event detector and 2) an event classification algorithm. The first one does not need to be trained and shows a detection rate up to 94%. The event classification algorithm uses a novel load signature based on trajectories of active, reactive, and distortion power (PQD) to obtain general models of appliance classes using principal component analysis. The F1 score and the F0.5 score (the last one is more relevant to activity monitoring) draw values of 90.6% and 98.5, respectively.

Ultrasonic local positioning system for mobile robot navigation: From low to high level processing

This paper presents the different levels of signal processing applied to an extensive Ultrasonic Local Positioning System (ULPS) intended for mobile robot (MR) indoor navigation. The working area is covered using several single LPSs, each of them composed of a set of closely arranged ultrasonic beacons. Low-level signal processing deals with the simultaneous encoded emission of the beacons that compose each single LPS and the detection carried out in each ultrasonic receiver on board the MR, with the aim of determining Differences of Times of Flights (DoTFs) to be applied to a positioning algorithm. High-level signal processing takes into account the positions obtained with each single ULPS to reference them to a global coordinate system and to perform navigation tasks in which the typical cumulative errors of MR odometer are corrected. The whole system has been developed and tested within the LORIS project.