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Innovative Sensor Technologies, State Estimation and Multisensor Data Fusion
Abstract and Figures
Innovative Sensor Technologies, State Estimation and Multisensor Data Fusion, a guest lecture I have in ECE 750 Intelligent Sensors and Sensor Networks at the University of Waterloo.
This talk describes multiplicity, multimodality, mobility, miniaturization, interoperability and accessibility as novel dimensions of sensor technologies.
Sensor web is described as an emerging standards to facilitate sensor interoperability and accessibility in system of systems.
Data imperfection aspects are discussed focusing on uncertainty in physical sensors. Bayes filter and Kalman filter are reviewed as mechanisms for state estimation under uncertainty.
Multisensor data fusion is presented as a way to handle data imperfection aspects and as a technology enabler for context-aware systems and Internet-of-Things.
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Data provided by sensors is always subjected to some level of uncertainty and inconsistency. Multisensor data fusion algorithms reduce the uncertainty by combining data from several sources. However, if these several sources provide inconsistent data, catastrophic fusion may occur where the performance of multisensor data fusion is significantly lower than the performance of each of the individual sensor. This
paper presents an approach to multisensor data fusion in order to decrease data uncertainty with ability to identify and handle inconsistency. The proposed approach relies on combining a modified Bayesian fusion algorithm with Kalman filtering. Three different approaches, namely, prefiltering, postfiltering and pre-postfiltering are described based on how filtering is applied to the sensor data, to the fused data or both. A case study to find the position of a mobile robot by estimating its x and y coordinates using four sensors is presented. The simulations show that combining fusion with filtering helps in handling the problem of uncertainty and inconsistency of the data.
Data management systems face several challenges in the sensor-rich worldwide web. These challenges must be solved to enable the worldwide sensor web vision. The overall problem's complexity results in various approaches to handling uncertain data, and several research work use probability theory as the basis for representing uncertainty. The data uncertainty is encoded in the form of probabilities, and the operations on the uncertainty are in accordance with the laws of probability theory. Probabilistic databases promise a systematic, intuitive alternative to handle such uncertainty. Sensor data contain numerous sources of noise and uncertainty, some of which are accessible only after spatial or temporal aggregation. These sources include those related to the sensor's physical coupling, its calibration, and actuation logic.
The entrepreneurship ecosystem at the University of Southern California (USC) began in the 1960s when the university off ered its fi rst courses dedicated to helping students understand the mindset and skills required to launch new businesses. The elaborate innovation and entrepreneurship ecosystem that exists at USC today was not the result of a decades-long strategy to develop such an ecosystem. Rather it emerged organically in much the same way as the fi eld of entrepreneurship, pushed by a growing demand from students, researchers and the community for a unifi ed system of resources and expertise that they could tap into as they developed their businesses or commercialized their research. This chapter explores the nature and evolution of the USC entrepreneurship ecosystem. The chapter is organized according to a modifi ed framework proposed by Hansen and Birkinshaw (2007) that describes the innovation value chain. It consists of three broad phases: idea generation, conversion and diff usion. Within each phase are activities that involve collaboration, screening and developing, and spreading ideas within and outside the organization. Refl ecting the three phases, the chapter comprises three parts: (1) the genesis of an entrepreneurship ecosystem; (2) the development of the innovation and entrepreneurship ecosystem; and (3) the diff usion of the entrepreneurial mindset and skills within and outside the university.
Data provided by sensors is always affected by some level of uncertainty or lack of certainty in the measurements. Combining data from several sources using multisensor data fusion algorithms exploits the data redundancy to reduce this uncertainty. This paper proposes an approach to multisensor data fusion that relies on combining a modified Bayesian fusion algorithm with Kalman filtering. Three different approaches namely: Pre-Filtering, Post-Filtering and Pre-Post-Filtering are described based on how filtering is applied to the sensor data, to the fused data or both. A case study of estimating the position of a mobile robot using optical encoder and Hall-effect sensor is presented. Experimental study shows that combining fusion with filtering helps in handling the problem of uncertainty and inconsistency of the data in both centralized and decentralized data fusion architectures.
This paper reports on ongoing research on development of data fusion systems capable of processing soft as well as hard data. Such fusion systems are distinguished from the conventional systems where input data are assumed to be provided by typically well-characterized electronic sensor systems. The incorporation of soft human-generated data into fusion process is an emerging trend in fusion community majorly motivated by asymmetric warfare situations where observational opportunities for traditional hard sensors is restricted. Random finite set theory is a mathematical framework with powerful representational and computational abilities making it a promising approach to address several fundamental challenges in soft/hard fusion systems. In this paper the first prototype soft/hard fusion system based on random finite set theory is described. Experimental results obtained using the developed system prove the plausibility as well as efficiency of a random finite set theoretic approach to fusion of soft/hard data.
We present a method for audio-visual speaker detection and tracking in a smart meeting room environment based on bearing measurements and particle filtering. Bearing measurements are determined using the Time Difference of Arrival (TDOA) of the acoustic signal reaching a pair of microphones, and by tracking facial regions in images from monocular cameras. A particle filter is used to sample the space of possible speaker locations within the meeting room, and to fuse the bearing measurements from auditory and visual sources. The proposed system was tested in a video messaging scenario, using a single participant seated in front of a screen to which a camera and microphone pair are attached. The experimental results show that the accuracy of speaker tracking using bearing measurements is related to the location of the speaker relative to the locations of the camera and microphones, which can be quantified using a parameter known as Dilution of Precision.