ChenYang Technologies GmbH & Co. KG works also in the area of digital signal processing in order to improve the reliability and measuring accuracy of measuring and testing systems. It develops digital signal processing methods and algorithms for its partners. The research and development works are focused on:

• Self-calibration measuring methods for precise measurement of electrical, geometric and mechanical quantities
• Self-correction algorithms for accuracy improvement of digital signal processing
• Adaptive filtering technique for noise suppression of measuring systems and telecommunication systems
• Precise Fourier analysis of electrical, acoustic, seismic and mechanical signals
• Precise parameter determination of damped oscillation signals.

A self-calibrated measuring method is based on a self-calibration with the use of internal reference elements and quantities. After the self-calibration the measuring errors are automatically corrected by digital signal processing algorithms, so that the measuring accuracy of the resulted measuring system can be improved in comparison with that of the original measuring system.

For measuring systems with a linear input-output relation, two reference elements are used for the self-calibration. The measuring result is determined by a linear interpolation using the measuring and reference data of the self-calibration. For measuring systems with a nonlinear input-output relation, the self-calibration needs three reference elements. The measuring result is determined by a quadratic interpolation. The measuring errors are compensated by the interpolation. Therefore, the measuring accuracy of a self-calibrated measuring system depends only on the tolerance of the reference elements, normally better than 0.1%.

Different methods e.g. analog and digital filter, averaging, smoothing and lock-in amplifier are used for noise reduction in order to improve the signal noise ratio of a measuring system. These methods, however, are only suitable for the reduction of noise, the spectrum of which is different from the signal spectrum. The problem is the reduction of noise parts, whose spectrum superimposes with the signal spectrum. This problem can be solved by using a frequency selective-adaptive filtering.