It has been demonstrated that the instantaneous acceleration associated with vibrating objects that are directly imaged by synthetic aperture radar (SAR) can be estimated through the application of the discrete fractional Fourier transform (DFrFT) using the information contained in the complex SAR image. In general, vibration signatures may include, for example, the number of chirped sinusoids as well as their respective base frequencies and chirp rates. By further processing the DFrFT-processed data for clutter-noise rejection by means of pseudo- subspace methods, has been shown that the SAR-vibrometry method can be reliable as long as the signal-to-noise ratio (SNR) and the signal-to-clutter ratio (SCR) of the slow-time SAR signal at the range-line of interest exceeds 15dB. Meanwhile, the Nyquist theorem dictates that the maximum measurable vibration frequency is limited by half of the pulse-repetition frequency. This paper focuses on the detection and estimation of vibrations generated by machinery concealed within buildings and other structures. This is a challenging task in general because the vibration signatures of the source are typically altered by their housing structure; moreover, the SNR at the surface of the housing structure tends to be reduced. Here, experimental results for three different vibrating targets, including one concealed target, are reported using complex SAR images acquired by the General Atomics Lynx radar at resolutions of 1-ft and 4-in. The concealed vibrating target is actuated by a gear motor with an off-balance weight attached to it, which is enclosed by a wooden housing. The vibrations of the motor are transmitted to a chimney that extends above the housing structure. Using the SAR vibrometry approach, it is shown that it is possible to distinguish among the three vibrating objects based upon their vibration signatures.