System Analysis of the Prospective Characteristics of the Helical Magnometric Detection Tool

This paper presents the results of the research of the helical transducer-based magnetometric detection tools. The authors have developed a mathematical model of signal formation, which enables calculation of useful signals at the output of the transducer for straight-line movement path of the object located at a random angle to the transducer’s direct axis and the random original coordinate. A statistical check of the degree of agreement of the simulated signals and full-scale measurements has been carried out. A representative base of the estimated and demonstrated signals has been created. A research program apparatus complex (PAC) was developed for the analysis of signals and prospective characteristics of detection tools, which enables simulation of operation of the decision box and evaluation of the prospective detection characteristics. The authors have surveyed the threshold parameter-oriented operation algorithm of the decision box. The paper demonstrates that the helical magnetometric detection tool with a peak-reading detector and a threshold decision-making algorithm works well in terms of detection of the objects with a relatively large magnetic moment. It discusses several types of algorithms based on artificial neural networks: error back-propagation (BP) neural network, bidirectional associative memory, and real-time recurrent networks. The real-time recurrent network, which ensures compliance of the tactical and technical characteristics of the product with the requirements for a perimeter detection tool, demonstrated the best detection characteristics. Thus, the paperdescribes a systematic analysis of the designspecifics and prospective characteristics of an advanced perimeter magnetometric detection tool based on a helical transducer.