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中文摘要：
针对加速度计信号降噪中精度有限、噪声统计规律不能完全确定等实际问题，提出了一种新的自适应卡尔曼滤波算法。利用新息自适应估计出了系统噪声和量测噪声的协方差阵，采用不同滑动窗口宽度设计了一组并行滤波器，通过加权优化获得了一个综合自适应滤波器，从而使该降噪方法不仅具有噪声自适应估计能力，而且对新息方差估计所需的滑动窗口宽度的选取进行了优化。理论上推导了该降噪算法的基本过程，并进行了加速度计实测数据降噪试验。试验结果表明，该降噪方法能获得较好的滤波效果，降噪后加速度计信号的噪声方差强度减少了5个数量级。
英文摘要：
To solve the problems in the accelerometer signal de-noising, such as the limited denoising precision and the uncertain of noise statistical distribution, a novel innovation-based adaptive Kalman filter algorithm is proposed. Based on the innovation sequence, the noise covariance of the process and the measurement can be adaptively estimated. A group of parallel filters is designed with moving windows of different widths. After the parallel filters are weighted, an integrated adaptive filter is formed. The derivation procedure of the algorithm and the de-noising experiment based on the real data from accelerometers are conducted. Results show that the algorithm has a good de-noising performance and decreases 5-order of magnitude of the noise.
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