Modulo sampling or unlimited sampling has recently drawn a great deal of
attention for cutting-edge applications, due to overcoming the barrier of
information loss through sensor saturation and clipping. This is a significant
problem, especially when the range of signal amplitudes is unknown or in the
near-far case. To overcome this fundamental bottleneck, we propose a
one-bit-aided (1bit-aided) modulo sampling scheme for direction-of-arrival
(DOA) estimation. On the one hand, one-bit quantization involving a simple
comparator offers the advantages of low-cost and low-complexity implementation.
On the other hand, one-bit quantization provides an estimate of the normalized
covariance matrix of the unquantized measurements via the arcsin law. The
estimate of the normalized covariance matrix is used to implement blind
integer-forcing (BIF) decoder to unwrap the modulo samples to construct the
covariance matrix, and subspace methods can be used to perform the DOA
estimation. Our approach named as 1bit-aided-BIF addresses the near-far problem
well and overcomes the intrinsic low dynamic range of one-bit quantization.
Numerical experiments validate the excellent performance of the proposed
algorithm compared to using a high-precision ADC directly in the given set up