Online measurement of blade tip clearance provides important data support for the performance evaluation and fault diagnosis of aero-engines. Most of the current clearance measurement technologies for aero-engines focus on measuring the clearance of individual blades, and no airborne application cases have been reported. This paper addresses the challenges associated with the capacitive blade tip clearance measurement method, such as high computational complexity, data redundancy, high sampling requirements, and difficulties in solving for the average clearance. A novel method for measuring average blade tip clearance based on the root mean square (RMS) of capacitive sensing signals is proposed. A blade tip clearance signal model based on RMS was developed, and the relationship between the RMS value of the clearance signal and the average blade tip clearance was derived. Simulations were performed to verify the impact of noise and time parameters on the RMS value of the clearance signal, and signal processing parameters for blade tip clearance based on the RMS method were introduced. A scaled blade model was constructed based on the constant duty cycle principle of the clearance signal, enabling dynamic calibration suitable for the RMS processing method. In-flight tests were conducted on the compressor of a specific aero-engine to validate the feasibility and effectiveness of the proposed method. Experimental results show that this method achieves average blade tip clearance measurement at a sampling rate of 10 kHz, with measurement errors less than 29 μm compared to traditional methods.