²⁵²Cf-source-driven nuclear material concentration identification based on compressive sensing

For solving the identification problem in 252Cf-source-driven noise analysis method, we used the compressive sensing theory and the nearest neighbor recognition algorithm, proposed a new classification method named CSKNN method, and then analysed identification probability. The experimental results show that for the classification and identification purposes, the CSKNN identification method only needs a few observations (the ratio between the number of measured values and the fission neutron signal length is no less than 0.1). When the signal to noise ratio increases, the recognition probability will converge faster to 100% and be less sensitive to K. Hence, the CSKNN method is reasonable and feasible, not only because it improves the real-time performance of nuclear arms control verification, but also effectively reduces the sampling cost. Most importantly, it provides a new theoretical basis and implementation method for the online classification of nuclear material concentration.