A real-time spoofing detection method using three low-cost collinear antennas in satellite navigation is proposed to address the limitations of traditional multi-antenna spoofing detection methods, which are limited in application and costly. The method leverages the collinearity of three low-cost antennas to constrain the observation equation, improving the accuracy of the pointing vector estimation. A binary statistical detection model based on the sum of squares (SSE) between the observed and estimated values of the pointing vector is employed for real-time spoofing detection. Simulation results show that the error of the skewness coefficient does not exceed 0.026, and experimental results demonstrate that the collinear antenna-based method reduces the standard deviation of the angle deviation of the pointing vector by over 55.62% in the presence of spoofing signals. Moreover, with a 1 m baseline, the method achieves 100% spoofing detection. The method does not require additional hardware, reducing hardware costs while enabling real-time spoofing detection. The proposed method uses three collinear antennas to estimate the pointing vector and employs a statistical model to detect spoofing signals. The method is validated through simulation and experimental results, showing its effectiveness in detecting spoofing signals. The method is compared with existing techniques, demonstrating its cost-effectiveness and scalability. The results indicate that the method achieves high detection probability, with a 1 m baseline achieving 100% detection. The method is suitable for applications requiring real-time spoofing detection with low hardware costs.A real-time spoofing detection method using three low-cost collinear antennas in satellite navigation is proposed to address the limitations of traditional multi-antenna spoofing detection methods, which are limited in application and costly. The method leverages the collinearity of three low-cost antennas to constrain the observation equation, improving the accuracy of the pointing vector estimation. A binary statistical detection model based on the sum of squares (SSE) between the observed and estimated values of the pointing vector is employed for real-time spoofing detection. Simulation results show that the error of the skewness coefficient does not exceed 0.026, and experimental results demonstrate that the collinear antenna-based method reduces the standard deviation of the angle deviation of the pointing vector by over 55.62% in the presence of spoofing signals. Moreover, with a 1 m baseline, the method achieves 100% spoofing detection. The method does not require additional hardware, reducing hardware costs while enabling real-time spoofing detection. The proposed method uses three collinear antennas to estimate the pointing vector and employs a statistical model to detect spoofing signals. The method is validated through simulation and experimental results, showing its effectiveness in detecting spoofing signals. The method is compared with existing techniques, demonstrating its cost-effectiveness and scalability. The results indicate that the method achieves high detection probability, with a 1 m baseline achieving 100% detection. The method is suitable for applications requiring real-time spoofing detection with low hardware costs.