{"product_id":"time-varying-frequency-spectral-estimation-extraction-von-hall-steven","title":"Time-varying frequency\/spectral estimation extraction","description":"\u003cp\u003eA time-varying autoregressive (TVAR) approach is used for modeling nonstationary signals, and frequency information is then extracted from the TVAR parameters. Two methods may be used for estimating the TVAR parameters: the adaptive algorithm approach and the basis function approach. Adaptive algorithms, such as the least mean square (LMS) and the recursive least square (RLS), use a dynamic model for adapting the TVAR parameters and are capable of tracking time-varying frequency, provided that the variation is slow. It is observed that, if the signals have a single timefrequency component, the RLS with a fixed pole on the unit circle yields the fastest convergence. The basis function method employs an explicit model for the TVAR parameter variation, and model parameters are estimated via a block calculation. We proposed a modification to the basis function method by utilizing both forward and backward predictors for estimating the time-varying spectral density of nonstationary signals. It is shown that our approach yields better accuracy than the existing basis function approach, which uses only the forward predictor.\u003c\/p\u003e\u003cdiv class=\"aw-variant-hidden-subtitle-div\" id=\"aw-variant-subtitle-9783838340753\"\u003e\u003ch3\u003eAdaptive algorithm vs. Basis Function method\u003c\/h3\u003e\u003c\/div\u003e","brand":"Autorenwelt Shop","offers":[{"title":"Softcover - 9783838340753","offer_id":39498923311197,"sku":"9783838340753","price":59.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0940\/0622\/files\/c051c560-8fa7-4efe-b294-4755eeeb2e55.jpg?v=1770790825","url":"https:\/\/shop.autorenwelt.de\/products\/time-varying-frequency-spectral-estimation-extraction-von-hall-steven","provider":"Autorenwelt Shop","version":"1.0","type":"link"}