Real-time mixed denoising method based on wavelet threshold, median filtering and mean filtering
A wavelet threshold denoising and mean filtering technology, used in navigation computing tools and other directions, can solve the problems that cannot meet the requirements of high-precision real-time systems, the denoising effect is not as good as wavelet threshold denoising, and the denoising effect is reduced, so as to overcome the filtering time. The effect of lengthening, reducing data length, and increasing data length
Inactive Publication Date: 2012-12-19
BEIHANG UNIV
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[0008] In summary, among the algorithms that can be used for real-time denoising of MEMS sensors, the denoising effect of wavelet threshold denoising is relatively optimal, but if it is applied to real-time systems, it is necessary to reduce the number of layers of wavelet decomposition and the length of data. In this way, the denoising effect will be greatly reduced; the denoising time of mean filter and median filter is shorter, which is more suitable for real-time systems, but the denoising effect is not as good as wavelet threshold denoising
The above three methods all have obvious shortcomings. If used alone, none of them can meet the needs of high-precision real-time systems.
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[0027] The real-time hybrid denoising method in the present invention is proposed according to the problem that the MEMS sensor output signal is noisy, and the denoising effect of the existing real-time denoising method is not good. The latest segment of real-time data is subjected to noise reduction processing, so it can be used for real-time noise reduction processing on the static and dynamic output signals of MEMS sensors; the algorithm based on SIGMA analysis is used to adaptively determine the number of layers of wavelet decomposition, so that it can ensure that the useful signal does not Under the premise of loss, the random noise is filtered out to the maximum extent. The filtering method combining median filtering and mean filtering is used to further process the signal after wavelet threshold denoising. The window lengths of median filtering and mean filtering are set to extremely small values, so it is also suitable for static and dynamic output signals real-time de...
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The invention discloses a real-time mixed denoising method based on wavelet threshold denoising, median filtering and mean filtering, and the method comprises the following steps: (1) firstly, determining the processing length n of data subjected to wavelet transform according to the real-time requirements of a system, clipping the latest section of an output signal of an MEMS (micro electro mechanical system) sensor, and processing the section of sequence by using a wavelet threshold denoising method so as to filter out noises and improve the signal-to-noise ratio, wherein (j0 belongs to N, N is an integer set, j0 is greater than or equal to 1, and the length of the latest section is n; (2) according to the real-time requirement of the system, intercepting the latest section of the sequence subjected to wavelet threshold denoising, and carrying out mean filtering on the section of sequence; and (3) carrying out median filtering on the output sequence subjected to mean filtering. The method disclosed by the invention has the advantages that under the condition of ensuring the real-time requirement of the system, the real-time denoising effect of the MEMS sensor is improved, and the output precision of the MEMS sensor is increased.
Description
technical field [0001] The invention relates to the field of real-time denoising of output signals of MEMS (Micro Electro Mechanical System, Micro Electro Mechanical System) inertial sensors, in particular to a real-time hybrid denoising method of MEMS sensors based on wavelet threshold denoising, median filtering and mean filtering. Background technique [0002] In recent years, with the development of MEMS technology, MEMS inertial sensors have been widely used in the field of navigation and positioning. Its small size, light weight, and low cost meet the basic requirements of navigation systems in most commercial applications, so it has been more and more widely used in low-cost inertial navigation systems. However, limited by the manufacturing process and technical level, the accuracy of MEMS sensors is low at present, and their output signals usually contain a lot of noise and are non-stationary, and include the influence of the test environment. If not effectively remo...
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IPC IPC(8): G01C21/20
Inventor 秦红磊丛丽张亚珍
Owner BEIHANG UNIV