Adaptive Signal Processing Algorithms Research Articles

Robust MR AS-type algorithm for system identification

A robust adaptive algorithm for system identification and adaptive signal processing is derived. The algorithm can be viewed as a combination of the output-error-type and equation-error-type identifiers. No distributional assumptions on the noise term have been made. This approach allows computational advantages in obtaining the estimates, and it also allows us to identify any structural change.

Noncanonical FIR filters and adaptive signal processing

The letter analyses the use of noncanonical FIR filters, such as the Inmos A100 cascadable signal processing chip, for adaptive signal processing algorithms. The noncanonical structure is functionally equivalent to the noncanonical FIR filter in a fixed-weight system; however, gradient-based adaptive algorithms cannot be exactly implemented using the noncanonical FIR filter, and therefore the classical algorithms must be reformulated and reanalysed for both convergence and stability.

New MRAS-type algorithm for system identification and adaptive signal processing

A new adaptive algorithm for system identification and adaptive signal processing has been proposed in this paper. The algorithm can be viewed as a combination of the output-error type and the equation-error type of identifier (that is, a combination of the IIR adaptive filter and the FIR adaptive filter in signal-processing applications). It has been shown both mathematically and by computer simulation that the proposed algorithm can guarantee the globally asymptotical stability. The transient behaviour of the algorithm is better than the conventional output-error type of identifier, especially in the case where the system (signal model) has its eigenvalues on the unit circle. Under certain conditions on the system model (signal model), no moving average (MA) smoothing of the generalized error is needed for the strictly positive realness (SPR) conditions.

Tracking properties and steady-state performance of RLS adaptive filter algorithms

Adaptive signal processing algorithms derived from LS (least squares) cost functions are known to converge extremely fast and have excellent capabilities to "track" an unknown parameter vector. This paper treats analytically and experimentally the steady-state operation of RLS (recursive least squares) adaptive filters with exponential windows for stationary and nonstationary inputs. A new formula for the "estimation-noise" has been derived involving second- and fourth-order statistics of the filter input as well as the exponential windowing factor and filter length. Furthermore, it is shown that the adaptation process associated with "lag effects" depends solely on the exponential weighting parameter λ. In addition, the calculation of the excess mean square error due to the lag for an assumed Markov channel provides the necessary information about tradeoffs between speed of adaptation and steady-state error. It is also the basis for comparison to the simple LMS algorithm, in a simple case of channel identification, it is shown that the LMS and RLS adaptive filters have the same tracking behavior. Finally, in the last part, we present new RLS restart procedures applied to transversal structures for mitigating the disastrous results of the third source of noise, namely, finite precision arithmetic.

Convergence Properties for a Family of Bounded Fixed Step-Size Algorithms

Convergence and tracking properties are discussed for a family of adaptive signal processing algorithms. The family treated includes bounded fixed gain versions of both the RM and KW stochastic approximation procedures. New results are presented that are applicable when the training data is correlated and the "optimal" solution is not unique and/or time-varying, The non-uniqueness is of interest in establishing "convergence" for constrained algorithms as well as determining "single-mode behavior".

An Adaptive Array Signal Processing Algorithm for Communications

An algorithm is described for initial synchronization in a communication system with a digital adaptive array. This algorithm can also be used for message extraction. A set of consecutive complex video samples of the array output is processed to obtain optimum adaptive array weights, based on a least mean square (LMS) error criterion. This computation is performed for each of the possible alternative signals which may be present during an observation interval. The correct synchronization time or message symbol is selected as the one which yields the minimum LMS error. Assuming orthogonality of the alternative codes, a probability distribution for the output of this processor has been derived.

Tracking properties of adaptive signal processing algorithms

Adaptive signal processing algorithms are often used in order to track an unknown time-varying parameter vector. This work develops an upper bound on the mean of the norm-squared error between the unknown parameter vector being tracked and the value obtained by the algorithm. The results require very mild covariance decay rate conditions on the training data and a bounded algorithm. The upper bound illustrates the relationship between the algorithm step size and the maximum rate of variation in the parameter vector being tracked.

Digital Control of High Performance Aircraft Using Adaptive Estimation Techniques

Since the early 1960's, a rapid advance in signal processing, including filtering and estimation techniques, has been evident. In contrast, applied feedback control, particularly for aircraft, is currently based on technology available prior to 1960, i. e., primarily either constant gain feedback or at most a standard gain-scheduling. In this paper, an adaptive signal processing algorithm is joined with gain-scheduling to produce an effective scheme for controlling the dynamics of high performance aircraft. A technique is presented for a reduced-order model (the longitudinal dynamics) of a high performance short-takeoff-and-landing (STOL) aircraft. The actual controller views the nonlinear behavior of the aircraft as being equivalent to a randomly switching sequence of linear models taken from a preliminary piecewise-linear fit of the system nonlinearities. The adaptive nature of the estimator is necessary to select the proper sequence of linear models along the flight trajectory. From the analysis of the reduced-order model the nonlinear behavior has been found to be well approximated by assuming an effective switching of the linear models at random times, the durations of which reflect the motion of the aircraft in response to pilot commands.