This study introduces and validates a novel approach for estimating measurement errors and implementing data fusion technology. The proposed method addresses the challenge of diminished accuracy in fusion outcomes from inertial measurement units (IMU) array in harsh conditions or during IMUs malfunction. The second-order difference (SOMD) algorithm is improved in this method, and a novel adaptive weighting coefficient construction method is proposed by combining the measurement error level and its similarity. Experimental results indicate that, in comparison to the Adaptive Kalman Filtering method based on the SOMD algorithm and the Weighted Average method, the method proposed in this paper enhances the suppression of multi-source noise such as zero-bias stability, zero-bias instability, and angle random walk in gyroscopes and accelerometers by over 40% under harsh environments or when IMUs fail. This demonstrates that the fusion algorithm proposed exhibits superior noise suppression capability and stability.