We present a two stage Direction of Arrival (DOA) technique that maintains a high resolution capability while preserving full array aperture processing in data limited scenarios. This is done by first synthetically expanding the temporal data for each sensor using a 2-D linear prediction technique.The second stage is to apply high resolution algorithms such as MUSIC or MVDR to this temporally extrapolated data set. Our results show that not only is the high resolution preserved for the MUSIC algorithm but the array power output also exhibit at least 5 dB lower sidelobes.

A new high-resolution virtual spectral imaging technique is able to resolve a closely spaced dominant and low-level sinusoidal signal (power spread of up to 15 dB) in harsh signal-tonoise ratio environments with limited samples where a highresolution 2-D autoregressive (AR) spectral estimation algorithm failed. This feat is accomplished by applying a 2-D fast Fourier transform to an expanded 2-D measurement data set (which consists of the original measurements extended by vectorextrapolated virtual measurements). The virtual measurement data are created from the original 2-D measurements using an innovative virtual-data vector extrapolation algorithm. A special 2-D AR model is used to model the measurements and then used to extrapolate measurements a vector at a time. Simulations compare our virtual spectral image with a high-resolution 2-D AR spectral image and a spectral image of the truth (the measurements that the extrapolation algorithm is trying to predict). The virtual measurement data are also compared with the true measurement data. We analytically show that the virtual measurements are a function of the true measurements plus some residual error terms. Mean-squared-error simulations show that the extrapolated measurements are well behaved for our problem space and provide a cost example.

No abstract

No abstract

In subspace based DOA algorithms estimating the signal or noise subspace accurately, is imperative as it is the foundation for which all such algorithms are built upon. Estimating the subspaces is achieved from decomposing a data matrix or the spatial covariance matrix each incurring a computational burden. We propose to reduce the computational complexity of estimating the noise subspace by using a computationally efficient covariance matrix estimate, whose multiplications are independent of data size. We investigate the effects of the non-linearity on the noise subspaces and to the DOA estimation using the MUSIC algorithm.