The inverse Toeplitz eigenvalue problem (ToIEP) concerns finding a vector that specifies the real-valued symmetric Toeplitz matrix with the prescribed set of eigenvalues. Since phase "calibration" errors in uniform linear antenna arrays (ULAs) do not change the covariance matrix eigenvalues and the moduli of the covariance matrix elements, we formulate a number of the new ToIEP problems of the Hermitian Toeplitz matrix reconstruction, given the moduli of the matrix elements and the matrix eigenvalues. We demonstrate that for the real-valued case, only two solutions to this problem exist, with the "non-physical" one that in most practical cases could be easily disregarded. The computational algorithm for the real-valued case is quite simple. For the complex-valued case, we demonstrate that the family of solutions is broader and includes solutions inappropriate for calibration. For this reason, we modified this ToIEP problem to match the covariance matrix of the uncalibrated ULA. We investigate the statistical convergence of the ad-hoc algorithm with the sample matrices instead of the true ones. The proposed ad-hoc algorithms require the so-called "strong" or "argumental" convergence, which means a large enough required sample volume that reduces the errors in the estimated covariance matrix elements. Along with the ULA arrays, we also considered the fully augmentable minimum redundancy arrays that generate the same (full) set of covariance lags as the uniform linear arrays, and we specified the conditions when the ULA Toeplitz covariance matrix may be reconstructed given the M-variate MRA covariance matrix.