Engineering topics

Brooks, Richard R

Publications and source records attributed to Brooks, Richard R.

3 records

DOE OSTI2016

Performance analysis of Wald-statistic based network detection methods for radiation sources

There have been increasingly large deployments of radiation detection networks that require computationally fast algorithms to produce prompt results over ad-hoc sub-networks of mobile devices, such as smart-phones. These algorithms are in sharp contrast to complex network algorithms that necessitate all measurements to be sent to powerful central servers. In this work, at individual sensors, we employ Wald-statistic based detection algorithms which are computationally very fast, and are implemented as one of three Z-tests and four chi-square tests. At fusion center, we apply the K-out-of-N fusion to combine the sensors hard decisions. We characterize the performance of detection methods by deriving analytical expressions for the distributions of underlying test statistics, and by analyzing the fusion performances in terms of K, N, and the false-alarm rates of individual detectors. We experimentally validate our methods using measurements from indoor and outdoor characterization tests of the Intelligence Radiation Sensors Systems (IRSS) program. In particular, utilizing the outdoor measurements, we construct two important real-life scenarios, boundary surveillance and portal monitoring, and present the results of our algorithms.

Sen, Satyabrata↗

DOE OSTI2016

A source-attractor approach to network detection of radiation sources

Radiation source detection using a network of detectors is an active field of research for homeland security and defense applications. We propose Source-attractor Radiation Detection (SRD) method to aggregate measurements from a network of detectors for radiation source detection. SRD method models a potential radiation source as a magnet -like attractor that pulls in pre-computed virtual points from the detector locations. A detection decision is made if a sufficient level of attraction, quantified by the increase in the clustering of the shifted virtual points, is observed. Compared with traditional methods, SRD has the following advantages: i) it does not require an accurate estimate of the source location from limited and noise-corrupted sensor readings, unlike the localizationbased methods, and ii) its virtual point shifting and clustering calculation involve simple arithmetic operations based on the number of detectors, avoiding the high computational complexity of grid-based likelihood estimation methods. We evaluate its detection performance using canonical datasets from Domestic Nuclear Detection Office s (DNDO) Intelligence Radiation Sensors Systems (IRSS) tests. SRD achieves both lower false alarm rate and false negative rate compared to three existing algorithms for network source detection.

Wu, Qishi↗

DOE OSTI2016

Datasets for radiation network algorithm development and testing

Domestic Nuclear Detection Office s (DNDO) Intelligence Radiation Sensors Systems (IRSS) program supported the development of networks of commercial-off-the-shelf (COTS) radiation counters for detecting, localizing, and identifying low-level radiation sources. Under this program, a series of indoor and outdoor tests were conducted with multiple source strengths and types, different background profiles, and various types of source and detector movements. Following the tests, network algorithms were replayed in various re-constructed scenarios using sub-networks. These measurements and algorithm traces together provide a rich collection of highly valuable datasets for testing the current and next generation radiation network algorithms, including the ones (to be) developed by broader R&D communities such as distributed detection, information fusion, and sensor networks. From this multiple TeraByte IRSS database, we distilled out and packaged the first batch of canonical datasets for public release. They include measurements from ten indoor and two outdoor tests which represent increasingly challenging baseline scenarios for robustly testing radiation network algorithms.

Rao, Nageswara S↗