gps spoofing detection using caf images and neural networks based on the proposed peak mapping dimensionality reduction algorithm and tcnn model

Global positioning system (gps)-based positioning has become an indispensable part of our daily lives. a gps receiver calculates its distance from a satellite by measuring the signal reception delay. then, after determining its position relative to at least four satellites, the receiver obtains its precise location in three dimensions. there is a fundamental flaw in this positioning system, namely that satellite signals at ground level are very weak and susceptible to interference in the bandwidth; therefore, even a slight interference can disrupt the gps receiver. in this paper, spoofing detection based on the cross ambiguity function (caf) is used. furthermore, a dimension reduction algorithm is proposed to improve the speed and performance of the detection process. the reduced-dimensional images are trained by a convolutional neural network (cnn). additionally, a modified cnn model as transformed-cnn (tcnn) is presented to enhance accuracy in this paper. the simulation results show a 98.67% improvement in network training speed compared to images with original dimensions, a 1.16% improvement in detection accuracy compared to the baseline model with reduced dimensions, and a 9.83% improvement compared to the original dimensions in detecting spoofing, demonstrating the effectiveness of the proposed algorithm and model.