High Resolution Full Aperture Processing of Data Limited Scenarios through Synthetically Extending the Temporal Data

Abstract

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.

Low-Level Sinusoidal Signal Detection From a High-Resolution Virtual Spectral Image Using Autoregressive Vector Extrapolation

“…Table III presents the computational complexity. Thus, the complexity for the vector-extrapolated virtual imaging algorithm is based on estimating the model in both dimensions, hence the maximum operation and the extrapolation and given by (17). Extrapolating the corners are incorporated in the row and column by definition of O, i.e., O (max (N 1 , N 2 …”

“…In [16] and [17], the authors used a 2-D AR quarter-plane model that extrapolated a single measurement at a time to extend the spatial and temporal dimensions, respectively. In [18], a high-resolution direction of arrival estimate was attained through a 2-D AR power spectrum (in wavenumber), which also improved the resolution for equally powered sources, but failed to resolve the closely spaced low-level source in the presence of a dominant source.…”

Constrained Sensor System Resolves Closely Spaced Obscured Low-SNR Sources Using Virtual Beamforming

“…A limitation of this extrapolation technique is that the 2-D AR QP model requires data that does not exist. It can be zero-filled (as was done in [15]) or extrapolated using a 1-D linear prediction technique as was done in [21] and [22]. A vector extrapolation technique [24] was applied by the authors to enhance spectral estimation in [17] and [18], which overcomes this limitation.…”

“…Third, we derive the SNR for this virtual system and show that, in the case of perfect measurement extrapolation, we are within a constant of the true SNR (if we had that many data measurements). Fourth, is that in the simulation we compared our algorithm with two highresolution algorithms and to two other extrapolation algorithms in [20]- [22]. In addition, the robustness to noise of the array output power is demonstrated through Monte Carlo simulation.…”

“…The extrapolated data is then used with algorithms in various applications such as radar [12]- [15], spectral estimation [16]- [19], DOA estimation in array processing [11]- [22], and target recognition [23].…”