To address the current issues of low accuracy and misidentification in indirect tire pressure monitoring systems (iTPMS) when detecting underinflation in all four wheels simultaneously, as well as the misidentification caused by engine vibrations, a study was conducted on the wheel speed spectral characteristics generated by road excitation. An indirect tire pressure monitoring algorithm based on wheel speed sensors and onboard hardware is proposed to improve the accuracy of underinflated tire identification. First, the wheel speed signal is preprocessed through signal denoising and gear error filtering to eliminate the issues of gear skipping and multi-tooth interference. The gear ring error is corrected using the Recursive Least Squares (RLS) method. Then, combined with the analysis of tire vibration characteristics, the Fast Fourier Transform (FFT) method is used to obtain the spectral characteristics of the wheel speed. Band-pass filters (BSP) and notch filters (NF) are applied to acquire the wheel speed spectral characteristics within a specified range, eliminating the influence of engine rotation. The tire pressure status is determined by the resulting resonance frequency peaks, where the peak frequency of an underinflated tire is 2-3 Hz lower than that of a normally inflated tire. Based on this characteristic, the tire pressure result is provided. Real vehicle test results indicate that this algorithm can eliminate the impact of engine rotation on wheel speed, ensuring the identification accuracy of single, dual, and triple underinflated wheels, as well as accurately identifying simultaneous underinflation in all four wheels. The condition recognition capability increases by approximately 18%, and the accuracy of identifying conditions where engine speed affects tire resonance improves by about 25%. Compared to traditional indirect tire pressure monitoring, this algorithm can more accurately and promptly inform the driver to avoid the risk of tire blowouts.