In general, currently employed vehicle classification algorithms based on the magnetic signature can distinguish among only a few vehicle classes. The work presents a new approach to this problem. A set of characteristic parameters measurable from the magnetic signature and limits of their uncertainty intervals are determined independently for each predefined class. The source of information on the vehicle parameters is its magnetic signature measured in a system that enables independent measurement of two signals, i.e. changes in the active and reactive component of the inductive loop impedance caused by a passing vehicle. These innovations result in high selective classification system, which utilizes over a dozen vehicle classes. The evaluation of the proposed approach was carried out for good vehicles consisting of 2-axle tractor and a 3-axle semi-trailer.

The work proposes a new method for vehicle classification, which allows treating vehicles uniformly at the stage of defining the vehicle classes, as well as during the classification itself and the assessment of its correctness. The sole source of information about a vehicle is its magnetic signature normalised with respect to the amplitude and duration. The proposed method allows defining a large number (even several thousand) of classes comprising vehicles whose magnetic signatures are similar according to the assumed criterion with precisely determined degree of similarity. The decision about the degree of similarity and, consequently, about the number of classes, is taken by a user depending on the classification purpose. An additional advantage of the proposed solution is the automated defining of vehicle classes for the given degree of similarity between signatures determined by a user. Thus the human factor, which plays a significant role in currently used methods, has been removed from the classification process at the stage of defining vehicle classes. The efficiency of the proposed approach to the vehicle classification problem was demonstrated on the basis of a large set of experimental data.