An adaptive identification system, for identifying a propagation system characteristic by an adaptive filter, includes a signal generator that generates an identification input signal including a frequency component of an integer multiple of a fundamental frequency and having a periodicity satisfying a PE condition, a setting unit that sets moving average time to a fundamental period of the identification input signal, and an adaptive algorithm execution unit that uses a moving average value and a diagonal matrix to update a coefficient of the adaptive filter, the moving average value being obtained by calculating a moving average of a cross-correlation vector of a vector of the identification input signal and an observation signal with the moving average time, and the diagonal matrix being obtained by diagonalizing a matrix obtained by calculating a moving average of an autocorrelation matrix of the vector of the identification input signal with the moving average time.

A method and a system of distributed estimation using q-diffusion least mean squares (qDiff-LMS) to modify adaptive filter weights in a decentralized wireless sensor network of N nodes is described. The method includes receiving, at each node, k, a local estimate of a previous time instance weight, w.sub.k(i−1), of an adaptive filter of each neighboring node, l, where l=1, 2, . . . , M, combining the local estimates of the previous time instance weights to generate a linear combination of global diffused weights, Ø.sub.k(i−1), measuring, for each node k, an output, y.sub.k(i), of the adaptive filter of the node k, calculating, for each node k, a desired response, d.sub.k(i); generating, for each node k, an estimation error, e.sub.k.sup.CTA(i) by subtracting the output, y.sub.k(i) from the desired response, d.sub.k(i), and updating the global diffused weights by adding a portion of the estimated error to the global diffused weights.

A method for adaptive signal processing is provided. In the method, a second vector is obtained by initializing a first vector without regularization of a cost function. The cost function is regularized with the first vector and the second vector as variables. The first vector is updated based on an input signal, according to the regularized cost function. Then, an output signal is provided based on the updated first vector. The second vector is updated based on the update of the first vector. An apparatus for adaptive signal processing is provided accordingly. The method and the apparatus are well compatible with existing adaptive signal processing. The convergence coefficients of the adaptive filter system become more stable. Moreover, impact of an extra penalty added to the cost function on a bias can be minimized, and the increased complexity of the system is very limited.

An adaptive identification system, for identifying a propagation system characteristic by an adaptive filter, includes a signal generator that generates an identification input signal including a frequency component of an integer multiple of a fundamental frequency and having a periodicity satisfying a PE condition, a setting unit that sets moving average time to a fundamental period of the identification input signal, and an adaptive algorithm execution unit that uses a moving average value and a diagonal matrix to update a coefficient of the adaptive filter, the moving average value being obtained by calculating a moving average of a cross-correlation vector of a vector of the identification input signal and an observation signal with the moving average time, and the diagonal matrix being obtained by diagonalizing a matrix obtained by calculating a moving average of an autocorrelation matrix of the vector of the identification input signal with the moving average time.

A method for adaptive signal processing is provided. In the method, a second vector is obtained by initializing a first vector without regularization of a cost function. The cost function is regularized with the first vector and the second vector as variables. The first vector is updated based on an input signal, according to the regularized cost function. Then, an output signal is provided based on the updated first vector. The second vector is updated based on the update of the first vector. An apparatus for adaptive signal processing is provided accordingly. The method and the apparatus are well compatible with existing adaptive signal processing. The convergence coefficients of the adaptive filter system become more stable. Moreover, impact of an extra penalty added to the cost function on a bias can be minimized, and the increased complexity of the system is very limited.

An adaptive filter calculates frequency domain coefficients and in the frequency domain dynamically adjusts a leakage/step size parameter that controls adaptation of the adaptive filter based on the calculated frequency domain coefficients (e.g., based on a peak magnitude of the coefficients among frequency bins or on the magnitude of the coefficient of the corresponding frequency bin). The adaptive filter calculates the coefficients based on frequency domain input and error signals, dynamically adjusts a frequency domain coefficient magnitude limit parameter based on the calculated frequency domain coefficients (e.g., approximately proportionally to a peak magnitude of the coefficients among frequency bins) and uses the dynamically adjusted frequency domain coefficient magnitude limit parameter to limit a magnitude of the calculated frequency domain coefficients. The limit may be engaged above a frequency bin based on the peak magnitude frequency bin. An ANC system may employ the filter.

A receiving device includes: a resampler to convert a sampling rate of a reception signal, and output a first signal that is a signal having been subjected to sampling rate conversion; an equalizer to perform an adaptive equalization process using the first signal as an input, and output a second signal that is a signal having been subjected to the adaptive equalization process and having a sampling rate that is an integer fraction of an input signal; a correlation calculator to calculate a correlation function between the first signal and the second signal; and a rate controller to control a rate conversion ratio for sampling rate conversion in the resampler on a basis of the correlation function.

A receiving device includes: a resampler to convert a sampling rate of a reception signal, and output a first signal that is a signal having been subjected to sampling rate conversion; an equalizer to perform an adaptive equalization process using the first signal as an input, and output a second signal that is a signal having been subjected to the adaptive equalization process and having a sampling rate that is an integer fraction of an input signal; a correlation calculator to calculate a correlation function between the first signal and the second signal; and a rate controller to control a rate conversion ratio for sampling rate conversion in the resampler on a basis of the correlation function.