Sensor data fusion algorithms using square-root information filtering

Raol, Jitendra R.; Girija, G

doi:10.1049/ip-rsn:20020128

Cited by 27 publications

(17 citation statements)

References 3 publications

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“…In Figure 19 is shown the core PHP code of the Alarm controller (where the fusion of sensors' values is done). In the related literature there are several sensor and multisensory data fusion algorithms [51,52], but in this first step we use an easy data fusion algorithm which is based on a local database which generates the alarm if one of the sensor values is higher or lower than a threshold (although now we are introducing a smart decision and data fusion algorithm as we state in the conclusion section). In order to develop the data fusion system, we connected PHP with a MySQL local database.…”

Section: System Performancementioning

confidence: 99%

Ubiquitous Monitoring of Electrical Household Appliances

Lloret

Macías

Suárez

et al. 2012

Sensors

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The number of appliances at home is increasing continuously, mainly because they make our lives easier. Currently, technology is integrated in all objects of our daily life. TCP/IP let us monitor our home in real time and check ubiquitously if something is happening at home. Bearing in mind this idea, we have developed a low-cost system, which can be used in any type of electrical household appliance that takes information from the appliance and posts the information to the Twitter Social network. Several sensors placed in the household appliances gather the sensed data and send them wired or wirelessly, depending on the case, using small and cheap devices to a gateway located in the home. This gateway takes decisions, based on the received data, and sends notifications to Twitter. We have developed a software application that takes the values and decides when to issue an alarm to the registered users (Twitter friends of our smart home). The performance of our system has been measured taking into account the home network (using IEEE 802.3u and IEEE 802.11g) and the data publishing in Twitter. As a result, we have generated an original product and service for any electrical household appliance, regardless of the model and manufacturer, that helps home users improve their quality of life. The paper also shows that there is no system with the same innovative features like the ones presented in this paper.

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Section: System Performancementioning

confidence: 99%

Ubiquitous Monitoring of Electrical Household Appliances

Lloret

Macías

Suárez

et al. 2012

Sensors

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“…Finally, the authors have suggested that a new versions of PF with high efficiency and hybrid algorithms based on Kalman filter algorithm should be developed in future. Raol et al [17] presented the square-root information filter (SRIF) for application to sensor data fusion processes. A decentralized squareroot information filter (DSRIF) is proposed with the validation of the DSRIF using simulated data are given.…”

Section: Prior Work In the Fieldmentioning

confidence: 99%

Unmanned aerial vehicle navigation and mapping

Lazarus

Tsourdos

Silson

et al. 2008

Proceedings of the Institution of Mechanical Engineers, Part G:

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The unmanned aerial vehicle (UAV) has significant advantages over the ground vehicle, where it can achieve a high degree of manoeuvrability, high speed response time and the ability of large area coverage. The problem considered here is that of an UAV localization and mapping using an extended Kalman filter (EKF), interval analysis (IA), covariance intersection (CI) and Hough transform (HT) for a partially known environment. The map is known partially in the sense that the obstacles and the land-marks are known to some extent. The vehicle is localized with respect to the known obstacles and to recognise the unknown obstacles to update the map of the environment. The focus is to develop an approach which can give a guaranteed performance of sensor-based localization and mapping that would increase the safety of the aerial vehicle and to produce a better performance in building the map of the environment. The guaranteed performance is quantified by explicit bounds of the position estimate of the vehicle. Generally, the UAVs carry the required sensors such as inertial sensors, accelerometers, and gyroscopes, to measure the acceleration and the angular rate, while the obstacle detection and the mapmaking is carried out with time of flight sensors such as ultrasonic or laser sensors. Most of these sensors give overlapping or complementary information, which offers scope for exploiting data fusion. This task of data fusion is accomplished by combining the measurements from different sensors that are obtained from two different methods namely EKF and IA, and by processing these measurements with a data fusion algorithm using the CI principle. This fused information is used with the measurements from the time of flight sensors such as laser sensor to update the map of the environment by applying the HT technique. The algorithms are complementary in the sense that they compensate for each other's limitations, so that the resulting performance of the sensor system is better than of its individual components, which in turn, improves the accuracy and richness in updating the map of the partially known environment. This proposed intelligent sensor system can provide a mathematically provable performance guarantees that are achievable in practice.

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“…Various aspects of SID are treated in Ref. [8,15]. The ML=output error approach is found to be very popular for aircraft parameter estimation.…”

Section: Epiloguementioning

confidence: 99%

“…Certain time-varying properties of systems can be tracked by using online real-time system identificationcum-parameter estimation for use in adaptive control [6]; (2) Incorporation of ''soft computing'' into the traditional=conventional SIPEP. SIPEP can also play a significant role in this field [8]. SIPEP can also play a significant role in this field [8].…”

mentioning

confidence: 99%

Simulation of Flight Dynamics

2008

Flight Mechanics Modeling and Analysis

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Sensor data fusion algorithms using square-root information filtering

Cited by 27 publications

References 3 publications

Ubiquitous Monitoring of Electrical Household Appliances

Ubiquitous Monitoring of Electrical Household Appliances

Unmanned aerial vehicle navigation and mapping

Simulation of Flight Dynamics

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