  We consider a mixed analog-to-digital converter (ADC) based architecture
consisting of high-precision and one-bit ADCs with the antenna-varying
threshold for direction of arrival (DOA) estimation using a uniform linear
array (ULA), which utilizes fixed but different thresholds for one-bit ADCs
across different receive antennas. The Cram{\'e}r-Rao bound (CRB) with the
antenna-varying threshold is obtained. Then based on the lower bound of the
CRB, we derive the asymptotic CRB of the DOA, which depends on the placement of
mixed-ADC. Our analysis shows that distributing high-precision ADCs evenly
around the two edges of the ULA yields improved performance. This result can be
extended to a more general case where the ULA is equipped with two types of
ADCs with different quantization precisions. To efficiently obtain the maximum
likelihood DOA estimates, we propose a two-step algorithm. Firstly, we
formulate the model as a sparse signal representation problem, and modify the
sparse learning via iterative minimization (SLIM) approach to the mixed-ADC
based DOA estimation. In the second step, we use the relaxation-based approach
to cyclically refine the estimates of SLIM, further enhancing the DOA
estimation performance. Numerical examples are presented to demonstrate the
validity of the CRB analysis and the effectiveness of our methods.
