The traditional carrier-phase differential detection technology mainly relies on the spatial processing method, which uses antenna arrays or moving antennas to detect spoofing signals, but it cannot be applied to static single-antenna receivers. Aiming at this problem, this paper proposes a rotating single-antenna spoofing signal detection method based on the improved probabilistic neural network (IPNN). When the receiver antenna rotates at a constant speed, the carrier-phase double difference of the real signal will change with the incident angle of the satellite. According to this feature, the classification and detection of spoofing signals can be realized. Firstly, the rotating single-antenna receiver collects carrier-phase values and performs double-difference processing. Then, we construct an IPNN model, whose smoothing factor can be adaptively adjusted according to the type of failure mode. Finally, we use the IPNN model to realize the classification and processing of the carrier-phase double-difference observations and obtain the deception detection results. In addition, in order to reflect that the method has a certain practical value, we simulate the spoofing scenario of satellite signals and effectively identify abnormal satellite signals according to the detection results of the inter-satellite differential combination. Actual experiments indicate that the detection accuracy of the proposed method for spoofing signals reaches 98.84%, which is significantly better than the classical probabilistic neural network (PNN) and back-propagation neural network (BPNN), and the method can be implemented in fixed base station receivers for the real-time detection of forwarding spoofing.