摘要
Detection of the signal’s breakdown points is important for many science and engineering applications. Numerous signal processing methods have been used for this purpose. Of these, the adaptive prediction is simple and easy to implement, however;its simplicity and robustness are hindered by the required delay in the input signal. This paper introduces an efficient alternative to the adaptive prediction in the application of breakdown and inflection points’ detection. Unlike the adaptive predictor, the proposed filter doesn’t require a delay in the primary input to produce the filter’s reference input, which significantly improves the computation speed and overcome the problem of performance sensitivity to the delay value. The Normalized Least-Mean Squares algorithm was used to realize both the adaptive predictor and the proposed filter. The filters were implemented in LabVIEW system design software. The performances of the filters were studied using simulated signals and the simulation results were verified using an experimental signal. The simulation and experimental results showed that the proposed filter efficiently detects the signal breakdowns. Furthermore, the simplicity of the filter offered a significant improvement in the computation speed.
Detection of the signal’s breakdown points is important for many science and engineering applications. Numerous signal processing methods have been used for this purpose. Of these, the adaptive prediction is simple and easy to implement, however;its simplicity and robustness are hindered by the required delay in the input signal. This paper introduces an efficient alternative to the adaptive prediction in the application of breakdown and inflection points’ detection. Unlike the adaptive predictor, the proposed filter doesn’t require a delay in the primary input to produce the filter’s reference input, which significantly improves the computation speed and overcome the problem of performance sensitivity to the delay value. The Normalized Least-Mean Squares algorithm was used to realize both the adaptive predictor and the proposed filter. The filters were implemented in LabVIEW system design software. The performances of the filters were studied using simulated signals and the simulation results were verified using an experimental signal. The simulation and experimental results showed that the proposed filter efficiently detects the signal breakdowns. Furthermore, the simplicity of the filter offered a significant improvement in the computation speed.
