To monitor radioactivity passing through a vehicle such as a pedestrian, a car, a train or a truck, Radiation Portal Monitors (RMP) are commonly employed. These detection systems consist of a large volume detector set close to the potential source path. An alarm is then triggered when the signal rises over a threshold initially estimated in view of the natural background signal. The approach developed in this work makes use of several detectors in a network along the source path. The correlation detection approach is elaborated to take into account the temporal periodicity of the signals taken by all distributed sensors as a whole. This new detection method is then not based only on counting statistics but also on the temporal series analysis. Therefore, a specific algorithm has been developed in our laboratory for this security application and shows a significant improvement, especially in terms of detection probability increase and false alarm reduction. This paper presents the theoretical approach and promising results obtained by simulation.

Illegal radioactive material transportation detection, by terrorist for example, is problematic in urban public transportation. Academics and industrials systems include Radiation Portal Monitor (RPM) to detect radioactive matters transported in vehicles or carried by pedestrians. However, today's RPMs are not able to efficiently detect a radioactive material in movement. Due to count statistic and gamma background, false alarms may be triggered or at the contrary a radioactive material not detected. The statistical false alarm rate has to be as low as possible in order to limit useless intervention especially in urban mass transportation. The real-time approach depicted in this paper consists in using a time correlated detection technique in association with a sensor network. It is based on several low-cost and large area plastic scintillators and a digital signal processing designed for signal reconstruction from the sensor network. The number of sensors used in the network can be adapted to fit with applications requirements or cost. The reconstructed signal is improved by comparing other approaches. This allows us to increase the device speed that has to be scanned while decreasing the risk of false alarm.In the framework of a project called SECUR-ED Secured Urban Transportation -European Demonstration, this prototype system will be used during an experiment in the Milan urban mass transportation.

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This paper deals with a new generation of acquisition systems for nuclear instrumentation. This project, called PING aims at developing instrumentation devices in order to cover a wide range of nuclear measurements with single hardware architecture. More specifically, this device is well suited for neutron measurements. This system is based on a full digital signal processing free from any analog signal neither formatting nor processing. Digitized signals can then be processed for physical information extraction. The dedicated embedded signal processing software in Field Programmable Gate Array (FPGA) allows us to achieve versatile passive or active neutron measurements, or gamma spectrometry. First experimental results are reported in this article. Both gamma and neutron measurements performance are presented.

Counting measurements associated with nuclear instruments are tricky to carry out due to the stochastic process of the radioactivity. Indeed events counting have to be processed and filtered in order to display a stable count rate value and to allow variations monitoring in the measured activity. Smoothers (as the moving average) are adjusted by a time constant defined as a compromise between stability and response time.A new approach has been developed and consists in improving the response time while maintaining count rate stability. It uses the combination of a smoother together with a detection filter. A memory of counting data is processed to calculate several count rate estimates using several integration times. These estimates are then sorted into the memory from short to long integration times. A measurement position, in terms of integration time, is then chosen into this memory after a detection test.An inhomogeneity into the Poisson counting process is detected by comparison between current position estimate and the other estimates contained into the memory in respect with the associated statistical variance calculated with homogeneous assumption. The measurement position (historical time) and the ability to forget an obsolete data or to keep in memory a useful data are managed using the detection test result.The proposed smoother is then an adaptive and a learning algorithm allowing an optimization of the response time while maintaining measurement counting stability and converging efficiently to the best counting estimate after an effective change in activity. This algorithm has also the specificity to be low recursive and thus easily embedded into DSP electronics based on FPGA or micro-controllers meeting "real life" time requirements.