Enhanced detection and identification of radiological threats in cluttered environments

Shokhirev, Kirill N.; Cosofret, Bogdan R.; King, Michael C.; Harris, Bernard; Zhang, Chun; Masi, Daniel

doi:10.1109/ths.2012.6459899

Cited by 9 publications

(6 citation statements)

References 2 publications

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“…Furthermore, no alarms against other isotopes in the spectral library were obtained, thus affording a high level of isotope discrimination performance. 1 PCS demonstrated a significant increase in detection sensitivity at lower operational false alarms than the standard methods available on the TMI system. As a result, a new effort was initiated to integrate PCS with the TMI system and demonstrate improved capability while providing detection and identification alarms in real time.…”

Section: A Pcs Performance Evaluation: Sordsmentioning

confidence: 96%

“…1 Utilizing the NaI back and front arrays, a total of 35,000 two second live time spectra were generated from radiological background measurements collected over a 20 hour period while in transit from DC, through Philadelphia, to Boston. A subset of 6,000 spectra were used to estimate a PCS background model, while the remaining spectra were used to determine isotope specific thresholds for a Constant False Alarm Rate (CFAR) of 1 in 8 hours.…”

Section: A Pcs Performance Evaluation: Sordsmentioning

confidence: 99%

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Utilization of advanced clutter suppression algorithms for improved standoff detection and identification of radionuclide threats

et al. 2014

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Technology development efforts seek to increase the capability of detection systems in low Signal-to-Noise regimes encountered in both portal and urban detection applications. We have recently demonstrated significant performance enhancement in existing Advanced Spectroscopic Portals (ASP), Standoff Radiation Detection Systems (SORDS) and handheld isotope identifiers through the use of new advanced detection and identification algorithms.The Poisson Clutter Split (PCS) algorithm is a novel approach for radiological background estimation that improves the detection and discrimination capability of medium resolution detectors. The algorithm processes energy spectra and performs clutter suppression, yielding de-noised gamma-ray spectra that enable significant enhancements in detection and identification of low activity threats with spectral target recognition algorithms. The performance is achievable at the short integration times (0.5 -1 second) necessary for operation in a high throughput and dynamic environment. PCS has been integrated with ASP, SORDS and RIID units and evaluated in field trials. We present a quantitative analysis of algorithm performance against data collected by a range of systems in several cluttered environments (urban and containerized) with embedded check sources. We show that the algorithm achieves a high probability of detection/identification with low false alarm rates under low SNR regimes. For example, utilizing only 4 out of 12 NaI detectors currently available within an ASP unit, PCS processing demonstrated P d,ID > 90% at a CFAR (Constant False Alarm Rate) of 1 in 1000 occupancies against weak activity (7 -8µCi) and shielded sources traveling through the portal at 30 mph. This vehicle speed is a factor of 6 higher than was previously possible and results in significant increase in system throughput and overall performance.

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Section: A Pcs Performance Evaluation: Sordsmentioning

confidence: 96%

Section: A Pcs Performance Evaluation: Sordsmentioning

confidence: 99%

Utilization of advanced clutter suppression algorithms for improved standoff detection and identification of radionuclide threats

et al. 2014

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“…5) Radioisotope Identification: each target radioisotope has a signature histogram making radioisotope identification a histogram classification problem. Many algorithms can be applied to this kind of task, notably the Poisson-Clutter Split (PCS) algorithm [1] has been applied to the radioisotope identification histogram classification task in the past. In this frame-based algorithm described in Fig.…”

Section: A the Problemmentioning

confidence: 99%

Event-based Signal Processing for Radioisotope Identification

Huang

Jones

Zhang

et al. 2020

2020 6th International Conference on Event-Based Control, Communication, and Signal Processing (EBCCSP)

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“…The authors find that a careful combination of the two approaches can provide a noticeable improvement in performance while significantly reducing the necessary computation. The Poisson-Clutter Split (PCS) algorithm combines a Poisson distribution model (for background plus source) with a Generalized Likelihood Ratio Test (GLRT) to detect a specific source [6].…”

Section: Localizationmentioning

confidence: 99%

Detecting, identifying, and localizing radiological material in urban environments using scan statistics

Porter

Akakpo

2019

2019 IEEE International Symposium on Technologies for Homeland Security (HST)

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A method is proposed, based on scan statistics, to detect, identify, and localize illicit radiological material using mobile sensors in an urban environment. Our method handles varying levels of background radiation that change according to an (unknown) environment. Our method can accurately determine if a source is present along a street segment as well as identify which of six possible sources generated the radiation. Our method can also localize the source, when detected, to within a few seconds. We have presented our results across a range of decision thresholds allowing stakeholders to evaluate the performance at different false alarm rates. Due to the simplicity of our approach, our models can be trained in a few minutes with very little training data and holds the potential to score a run in real-time. Our method was one of the top performing submissions in the Detecting Radiological Threats in Urban Areas competition.

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Enhanced detection and identification of radiological threats in cluttered environments

Cited by 9 publications

References 2 publications

Utilization of advanced clutter suppression algorithms for improved standoff detection and identification of radionuclide threats

Utilization of advanced clutter suppression algorithms for improved standoff detection and identification of radionuclide threats

Event-based Signal Processing for Radioisotope Identification

Detecting, identifying, and localizing radiological material in urban environments using scan statistics

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