The electronically steerable passive array radiator (ESPAR) antenna is one kind of the parasitic elements based single-port output antennas with several variable reactances. It performs analog aerial beamforming and none of the signals on its passive elements can be observed. This fact and one that is more important--the nonlinear dependence of the output of the antenna from adjustable reactances--makes the problem substantially new and not resolvable by means of conventional adaptive array beamforming techniques. A novel approach based on stochastic approximation theory is proposed for the adaptive beamforming of the ESPAR antenna as a nonlinear spatial filter by variable parameters, thus forming both beam and nulls. Two learning rate schedule were examined about output SINR, stability, convergence, misadjustment, noise effect, bias term, etc., and the optimal one was proposed. Further development was traced. Our theoretic study, simulation results and performance analysis show that the ESPAR antenna can be controlled effectively, has strong potential for use in mobile terminals and seems to be very perspective.

The electronically steerable passive array radiator (ESPAR) antenna performs analog aerial beamforming that has only a single-port output and none of the signals on its passive elements can be observed. This fact and one that is more important--the highly nonlinear dependence of the output of the antenna from adjustable reactances--makes the problem substantially new and not resolvable by means of conventional adaptive array beamforming techniques. A novel approach based on stochastic approximation theory is proposed for the adaptive beamforming of the ESPAR antenna as a nonlinear spatial filter by variable parameters, thus forming both beam and nulls. Our theoretic study, simulation results and performance analysis show that the ESPAR antenna can be controlled effectively, has strong potential for use in mobile terminals and seems to be very perspective.

This paper provides a normalized Iterative Feedback Tuning (IFT) method that assures the boundedness of the gradient vector estimate (ρ) and the Hessian matrix estimate without the assumption that the internal signals are bounded. The proposed method uses the unbiased Gauss-Newton direction by the addition of the 4-th experiment. We also present blended control criteria and a PID-like controller as new design choices. In examples, the normalized IFT method results in a good convergence although the internal signal or the measurement noise variance is large.

In many actual applications of the adaptive filtering, input signals as well as output signals often contain observation noises. Hence, it is necessary to develop an adaptive filtering algorithm to such an errors-in-variables (EIV) model. One solution for identifying the EIV model is a total least squares (TLS) algorithm based on a singular value decomposition of an off-line processing. However, it has not been considered to identify the EIV IIR system using an adaptive TLS algorithm of which stability has been guaranteed during adaptation process. Hence we propose a normalized lattice IIR adaptive filtering algorithm for the TLS parameter estimation. We also show the effectiveness of the proposed algorithm under noisy circumstances through simulations.

In the actual sound environmental systems, it seems to be essentially difficult to exactly evaluate a whole probability distribution form of its response fluctuation, owing to various types of natural, social and human factors. We have reported a unified probability density expression in the standard expansion form of Hermite type orthonormal series taking a well-known Gaussian probability density function (abbr. p.d.f.) as the basis for generally evaluating non-Gaussian, non-linear correlation and/or non-stationary properties of the fluctuation phenomenon. However, in the real sound environment, there still remain many actual problems on the necessity of improving the above standard type probability expression for practical use. First, a central point in this paper is focused on how to find a new probabilistic theory of practically evaluating the variety and complexity of the actual random fluctuations, especially through newly introducing an equvivalence transformation toward the standard type probability expression mentioned above in the expansion form of Hermite type orthonormal series. Then, the effectiveness of the proposed theory has been confirmed experimentally too by applying it to the actual problems on the response probability evaluation of various sound insulation systems in an acoustic room.