Simulation Model of Mobile Detection Systems
In this paper, we consider a mobile source that we attempt to detect with man-portable, vehicle-mounted or boat-mounted radiation detectors. The source is assumed to transit an area populated with these mobile detectors, and the objective is to detect the source before it reaches a perimeter. We describe a simulation model developed to estimate the probability that one of the mobile detectors will come in to close proximity of the moving source and detect it. We illustrate with a maritime simulation example. Our simulation takes place in a 10 km by 5 km rectangular bay patrolled by boats equipped with 2-inch x 4-inch x 16-inch NaI detectors. Boats to be inspected enter the bay and randomly proceed to one of seven harbors on the shore. A source-bearing boat enters the mouth of the bay and proceeds to a pier on the opposite side. We wish to determine the probability that the source is detected and its range from target when detected. Patrol boats select the nearest in-bound boat for inspection and initiate an intercept course. Once within an operational range for the detection system, a detection algorithm is started. If the patrol boat confirms the source is not present, it selects the next nearest boat for inspection. Each run of the simulation ends either when a patrol successfully detects a source or when the source reaches its target. Several statistical detection algorithms have been implemented in the simulation model. First, a simple k-sigma algorithm, which alarms with the counts in a time window exceeds the mean background plus k times the standard deviation of background, is available to the user. The time window used is optimized with respect to the signal-to-background ratio for that range and relative speed. Second, a sequential probability ratio test [Wald 1947] is available, and configured in this simulation with a target false positive probability of 0.001 and false negative probability of 0.1. This test is utilized when the mobile detector maintains a constant range to the vessel being inspected. Finally, a variation of the sequential probability ratio test that is more appropriate when source and detector are not at constant range is available [Nelson 2005]. Each patrol boat in the fleet can be assigned a particular zone of the bay, or all boats can be assigned to monitor the entire bay. Boats assigned to a zone will only intercept and inspect other boats when they enter their zone. In our example simulation, each of two patrol boats operate in a 5 km by 5 km zone. Other parameters for this example include: (1) Detection range - 15 m range maintained between patrol boat and inspected boat; (2) Inbound boat arrival rate - Poisson process with mean arrival rate of 30 boats per hour; (3) Speed of boats to be inspected - Random between 4.5 and 9 knots; (4) Patrol boat speed - 10 knots; (5) Number of detectors per patrol boat - 4-2-inch x 4-inch x 16-inch NaI detectors; (6) Background radiation - 40 counts/sec per detector; and (7) Detector response due to radiation source at 1 meter - 1,589 counts/sec per detector. Simulation results indicate that two patrol boats are able to detect the source 81% of the time without zones and 90% of the time with zones. The average distances between the source and target at the end of the simulation is 5,866 km and 5,712 km for non-zoned and zoned patrols, respectively. Of those that did not reach the target, the average distance to the target is 7,305 km and 6,441 km respectively. Note that a design trade-off exists. While zoned patrols provide a higher probability of detection, the nonzoned patrols tend to detect the source farther from its target. Figure 1 displays the location of the source at the end of 1,000 simulations for the 5 x 10 km bay simulation. The simulation model and analysis described here can be used to determine the number of mobile detectors one would need to deploy in order to have a have reasonable chance of detecting a source in transit. By fixing the source speed to zero, the same model could be used to estimate how long it would take to detect a stationary source. For example, the model could predict how long it would take plant staff performing assigned duties carrying dosimeters to discover a contaminated spot in the facility.
- Research Organization:
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- W-7405-ENG-48
- OSTI ID:
- 958186
- Report Number(s):
- LLNL-CONF-410197; TRN: US1000363
- Resource Relation:
- Conference: Presented at: American Nuclear Society, Atlanta, GA, United States, Jun 14 - Jun 18, 2009
- Country of Publication:
- United States
- Language:
- English
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