Through-the-wall radar imaging is of value in several civilian and defense applications. One of the challenges in
through-the-wall radar imaging is the strong wall reflections which tend to persist over a long duration of time.
In order to image weak and close by targets behind walls, the wall reflections should be suppressed, or at least
be significantly alleviated. In this paper, we apply spatial filters across the antenna array to remove the spatial
zero-frequency and low-frequency components which correspond to wall reflections. The application of spatial
filters recognizes the fact that the wall EM responses do not significantly differ when viewed by the different
antennas along the axis of a real or synthesized array aperture which is parallel to the wall. The proposed
approach is tested with experimental data using solid wall, multi-layered wall, and cinder block wall. It is shown
that the wall reflections can be effectively reduced by spatial preprocessing prior to beamforming, producing
similar imaging results to those achieved when a background scene without the target is available.
Compressed sensing (CS) has recently attracted much interest because of its important offerings and versatility.
High-resolution radar imaging applications such as through-the-wall radar (TWR) imaging or inverse synthetic
aperture radar (ISAR) are two key application areas that can greatly benefit from CS. Both applications require
probing targets using radar signals with large bandwidth for collecting, and then processing, a large number of
data samples for achieving high resolution imaging. These applications are also characterized by sparse imaging
where targets of interest are few and have larger cross-section than clutter objects. Reducing the number of
samples without compromising the imaging quality reduces the acquisition time and saves signal bandwidth.
This reduction is important when surveillance is performed within small time window and when targets are
required to remain stationary without translation or rotation motions, to avoid blurring and smearing of images.
In this paper, we discuss applicability of compressed sensing to indoor radar imaging, using synthesized TWR
Through-the-wall imaging (TWI) is a challenging problem, even if the wall parameters and characteristics are
known to the system operator. Proper target classification and correct imaging interpretation require the application
of high resolution techniques using limited array size. In inverse synthetic aperture radar (ISAR), signal
subspace methods such as Multiple Signal Classification (MUSIC) are used to obtain high resolution imaging. In
this paper, we adopt signal subspace methods and apply them to the 2-D spectrum obtained from the delay-andsum
beamforming image. This is in contrast to ISAR, where raw data, in frequency and angle, is directly used
to form the estimate of the covariance matrix and array response vector. Using beams rather than raw data has
two main advantages, namely, it improves the signal-to-noise ratio (SNR) and can correctly image typical indoor
extended targets, such as tables and cabinets, as well as point targets. The paper presents both simulated and
experimental results using synthesized and real data. It compares the performance of beam-space MUSIC and
Capon beamformer. The experimental data is collected at the test facility in the Radar Imaging Laboratory,