Estimation of sensor detection probabilities with data from concurrent censors

by Donald Roy Barr

Publisher: Naval Postgraduate School in Monterey, Calif

Written in English
Published: Pages: 23 Downloads: 308
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  • Mathematical models,
  • Signal theory (Telecommunication),
  • Signal detection

About the Edition

When several sensors are concurrently scanning the same domain for signals, varying numbers of sensors may detect each signal. On some occasions, a signal may not be detected by any of the receivers. Using detection data collected from all the receivers over a period of scanning, it is possible to estimate the total number of signals that occurred in that period (including those that were not detected at all), as well as the detection probabilities for the individual receivers. Several estimators for these quantities are developed, in the contexts of several models concerning the signal generation process and the receiver behavior. (Author)

Edition Notes

Image sensor. Most popular combination for detection and tracking an object or detecting a human face is a webcam and the OpenCV vision software. This combination may be the best in detection and tracking applications, but it is necessary to have advanced programming skills and .   single parameter, Probability of Detection (POD), that will cover all contingent ranges of concentration, both zero and non-zero. This simplifi ed model allows the ability to compare probabilities across concentrations and further allows for a simple graphical representation of validation data as a POD curve graphed by. Distributed estimation An introduction to Sensor Networks In recent years, great attention has been devoted to multisensor data fusion for both military and civilian applications. Data fusion techniques combine data from multiple sensors and related information to . The real time probability density function (PDF) estimation of any environmental function from sensor network measurement is addressed. The sensor measurement data is modeled using Gaussian mixture PDFs and an algorithm is proposed to estimate the parameters by maximizing the log likelihood function of the sensor data. Here the real time probability density function (PDF) estimation of.

Data Sources We developed our system in Java. Figure 1 shows the overall architecture. Data are produced by sources; our sys-tem supports three types: real sensors, synthetic sources (e.g., generated by equations), and playback. We describe each next. Generally speaking, the data source is a real sensor net-work. ncsbook Decem CONTENTS v Event-based Control: Transmit When Necessary Further Reading Exercise Chapter Sensor Networks Introduction to Sensor Networks Sensor Scheduling Centralized Kalman Filtering Over a Static Sensor Tree Distributed Control over Sensor Networks Further Reading of sensor measurements are transmitted to the fusion center over error-free channels. They have proposed an energy-e cient iterative source localization scheme in which the algorithm begins with a coarse location estimate obtained from measurement data from a set of anchor sensors. Based on the accumulated information at each. Sensor The noun "sensor" means a detector of a stimulus (such as heat, light, motion, pressure). Example sentences with "sensor": An infrared sensor designed to detect movement triggered the roadside bomb. There is a shirt company making sensors that go into your clothing. They will watch how you sit, run, or ski and give data on that information.

which generalize the observability condition of centralized parameter estimation. The inter-sensor communication is quantized with random link (communication channel) failures. This is appro-priate, for example, in digital communication WSN when the data exchanges among a sensor and its neighbors are quantized, and the communication channels. Possible project topics: the EM algorithm and its applications, the Kalman filter and (one or more of) its applications, spectral estimation, white-spaces detection, distributed detection and estimation, sensor fusion, sequential detection and estimation, applications in your domain of interest (biology, image processing, optics, etc.), Markov. 0–9. ; 2SLS (two-stage least squares) – redirects to instrumental variable; 3SLS – see three-stage least squares; 68–95– rule; year flood.

Estimation of sensor detection probabilities with data from concurrent censors by Donald Roy Barr Download PDF EPUB FB2

Distributed Estimation and Detection for Sensor Networks Using Hidden model parameters from training data and specialize it to the event-region detection problem. This method is based on maximizing the product of the full conditional predictive probability density or mass functionsCited by: where n is the number of birds detected and p i is the detection probability of an individual bird.

The program MARK (White and Burnham ) provides the Horvitz-Thompson estimate of population size as a “derived parameter,” when using the “Huggins closed captures” data -observer data sets were analyzed using MARK with the Huggins closed captures data by: Gruenwald L., Chok H., and Aboukhamis M.

Using data mining to estimate missing sensor data. In Proc. 7th IEEE ICDM Workshop on Optimization-Based Data. Keywords: IEEE P; autonomous vehicle imaging systems; contrast detection probability Document Type: Research Article Publication date: 28 January This article was made available online on 28 May as a Fast Track article with title: "Detection probabilities: Performance prediction for sensors of autonomous vehicles".

Distributed detection in censoring sensor networks, where each sensor transmits “informative” observations to the Fusion Center (FC), and censors those deemed “uninformative”, has been. sensor data sets, we are able to study the detection performance of these methods.

We find that these methods sit at different points on the accurac y/robustness spectrum. While rule-based methods can detect and classify faults, they canbe sensitive to the choice of parameters. By contrast, the estimation method we study can tolerate errors in. Kumar and S.-J.

Park / Probability model for data redundancy detection in sensor networks in duplicate data delivery. Advantage of redundant data deliveries has also been exploited in wireless ad hoc networks [2,9,19].

Therefore, it is necessary to derive a mathematical model which can capture. Liang J. et al.: Distributed state estimation in sensor networks with randomly occurring nonlinearities subject to time delays.

ACM Transactions on Sensor Networks (TOSN), 9(1), 4 (). State Estimation and Anomaly Detection in WSN 17 Google Scholar. Here an image restoration on the basis of pixel simultaneous detection probabilities (PSDP) is proposed. These probabilities can be precisely determined by means of.

Distributed Detection and Estimation in Wireless Sensor Networks: Resource Allocation, Fusion Rules, and Network Security Edmond Nurellari Submitted in accordance with the requirements for the degree of Doctor of Philosophy The University of Leeds School of.

a centralized approach, the sensors need to send all their raw observations to the fusion center that then makes a decision. In signal processing and information theory[8], the field of distributed detection and estimation, which has a relatively long history (e.g. [8]–[13]), builds an elegant theoretical.

detection and estimation using multiple sensors, which may be geographically dispersed [2]. In classical multisensor detection and estimation, it is assumed that all the local sensors (such as radar, sonar, infrared) communicate all their data to a central processor that performs optimal detection and tracking of targets based on conventional.

However, for some not so sensitive areas, relatively low detection probabilities are required to reduce the number of deployed sensors (e.g. Figure ). For example, in a fire-detection system, high detection probabilities (close to 1) are requested for high-risk areas (e.g.

those close to chemical deposits), and low detection probabilities. Detection probability obtained from such an analysis of aggregated device data can be viewed as the product of the probabilities of presence at the local sampling station and detection, conditional on presence at the local sampling station, pj=θj (1−∏d=1D (1−pj [d])).

From: Occupancy Estimation and Modeling (Second Edition), Estimation of probability of detection (POD) curves by NDT typically relies on the manufacture of large numbers of realistic defect specimens, followed by practical trials of the inspection procedure.

These are costly and time consuming activities. Library of Congress Cataloging-in-Publication Data Barkat, Mourad. Signal detection and estimation/Mourad Barkat.—2nd ed.

Includes bibliographical references and index. ISBN 1. Signal detection. Stochastic processes. Estimation theory. Radar. Title. TKB '2—dc22 Inertial sensor-based method was suggested to be an option for estimating joint moments of the trunk segments.

Inertial sensors were also shown to be useful for the bottom-up estimation method using measured GRFs, in which average RMS values and average correlation coefficients were about Nm/kg and larger than about for all joints. encoded before transmission. In principle, the number of bits used in each sensor should depend on the accuracy of the data acquisition on that sensor.

At the same time, the number of bits transmitted per each channel use is upper bounded by the channel capacity, which depends on the transmit power and on the channel between sensor and sink node. data obtained at different times and places may lead to erroneous conclusions.

Adoption of new survey meth-ods that accurately estimate detection probabilities would alleviate this concern. The recent flurry of publications on detection prob-ability (Nichols et al.Buckland et al.Bart.

ESTIMATING DETECTION PROBABILITIES FROM MULTIPLE-OBSERVER POINT COUNTS M W. A, 1,4 K H. P, 2 T R. S 3 1Departments of Zoology and Biomathematics, Campus BoxNorth Carolina State University, Raleigh, North CarolinaUSA; 2Departments of Zoology, Biomathematics, and Statistics, Campus BoxNorth Carolina State University, Raleigh, North.

Robust peak detection algorithm (using z-scores) I came up with an algorithm that works very well for these types of datasets. It is based on the principle of dispersion: if a new datapoint is a given x number of standard deviations away from some moving mean, the algorithm signals (also called z-score).The algorithm is very robust because it constructs a separate moving mean and deviation.

Sensor data were periodically pushed to a central base station, where, an algorithm determined activity by associating the data with a specific configuration.

Similarly, Huang and Mao [ 7 ] proposed a hybrid detection method using the combination of CO 2 and light sensors by which the measurement results of CO 2 and light levels are transmitted.

This work addresses distributed data-reduction at sensor nodes using a combination of measurement-censoring and measurement quantization. The WSN is envisioned for de- The application of statistical signal processing techniques to detection and estimation tasks. quantization.

Remark 2. There are two distinct strategies of networked sensor fusion. The first is the centralized strategy [41,42], i.e., all sensors transmit their measurements to a fusion centre, which is responsible for integrated data processing for state estimation and/or parameter second is the distributed strategy [], i.e., each sensor processes its own measurements and then.

– digital signal: e.g. data, text b) Source Encoder Objective: Represent the source signal as efficiently as possible, i.e., with as few bits as possible ⇒ minimize the redundancy in the source encoder output bits Schober: Signal Detection and Estimation.

Introduction to Detection Theory We assume a parametric measurement model p(x|θ) [or p(x; θ), which is the notation that we sometimes use in the classical setting]. In point estimation theory, we estimated the parameter θ ∈ Θ given the data x.

Suppose now that we choose Θ 0 and Θ 1 that form a partition of the parameter space Θ: Θ 0. This is an interesting resource for data scientists, especially for those contemplating a career move to IoT (Internet of things).

Many of these modern, sensor-based data sets collected via Internet protocols and various apps and devices, are related to energy, urban planning, healthcare, engineering, weather, and transportation sectors. In distributed estimation, sensor nodes locally process their observed data and send the resulting messages to a sink, which combines the received messages to produce a final estimate of the unknown parameter.

In this dissertation, this problem is generalized and called "collaborative estimation", where some sensors can potentially have access. Detection Probabilities: Performance Prediction for Sensors of Autonomous Vehicles Authors: Geese, Marc ; Seger, Ulrich ; Paolillo, Alfredo Source: Electronic Imaging, Autonomous Vehicles and Machinespp.

(14). Recall that ifX is exponentially distributed with meanμ,then p X(x)= 1 μ exp − x μ,x≥ 0. (5) Suppose that one queue is being monitored. A message enters at time t = 0 and exits at time t = T. Under Hypothesis H1, T is an exponentially distributed random variable with mean μ1; under H0, T is exponentially distributed with mean that μ1 >μ0.

This work addresses distributed data-reduction at sensor nodes using a combination of measurement-censoring and measurement quantization. The WSN is envisioned for decentralized estimation of either a vector of unknown parameters in a maximum likelihood framework, or, for decentralized estimation of a random signal using Bayesian optimality.

Sensors do not change output state immediately when an input parameter change occurs. Rather, it will change to the new state over a period of time, called the response time (T r in Figure 5).

The response time can be defined as the time required for a sensor output to change from its previous state to a final settled value within a tolerance.DISTRIBUTED ESTIMATION IN SENSOR NETWORKS WITH IMPERFECT MODEL INFORMATION: AN ADAPTIVE LEARNING-BASED APPROACH Qing Zhou 1, Soummya Kar 2, Lauren Huie 3, and Shuguang Cui 1 1 Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 2 Department of Electrical and Computer Engineering, Carnegie Mellon University.