Although subspace-based methods for estimating the Angle of Arrival (AOA) require a precise array response to achieve highly accurate results, it is difficult to obtain this response in practice even though the antennas are calibrated. Therefore, a method of compensating for errors in calibration is required. This paper proposes a procedure to enable precise AOA estimates to be obtained in a real system by applying array calibration and spatial smoothing preprocessing (SSP). Measured data were collected from experiments using two scenarios, i.e., in an anechoic chamber and at an open site, where a single source signal arrived at the array antenna. All measured data were then calibrated by using data obtained at 0 deg in an anechoic chamber before the AOAs were estimated. Nevertheless, errors in the array response remained after calibration because errors in the AOA estimates could still be observed. SSP was then applied to the calibrated data to obtain more accurate AOA estimates. We found that SSP can reduce the random error in an array response obtained in a real system, leading to reduced errors in AOA estimates in the observed data. To generalize the problem that SSP can reduce random perturbation in the array response, simple expressions are illustrated and verified by Monte-Carlo simulation. Random gain and phase errors in the array response are only considered in this paper and ESPRIT was used to estimate the AOAs.

In this paper, a method of the signal subspace interpolation to constructing a continuous fingerprint database for radio localization is proposed. When using the fingerprint technique, enhancing the accuracy of location estimation requires very fine spatial resolution of the database, which entails much time in collecting the data to build up the database. Interpolated signal subspace is presented to achieve a fine spatial resolution of the fingerprint database. The angle of arrival (AOA) and the measured signal subspace at known locations are needed to obtain the interpolated signal subspaces. The effectiveness of this method is verified by an outdoor experiment and the estimated location using this method was compared with those using the geometrically calculated fingerprint and the measured signal subspace fingerprint techniques.