NUST Institutions Library Catalogue

Through wall imaging has wide range of civil and military applications (like attaining hostage or
terrorist information, weapons, explosive materials, building structures, detection buried people etc).
Through wall imaging process involve a wall as an obstacle, which causes strong wall reflections
which are termed as clutter. Moreover, due to reflections from surrounding and some other
artifacts (like system and false targets etc) noise components are also present in the received signals.
To mitigate, the clutters and noise a robust pre-processing (image enhancement) step is essential.
A review of state of the art through wall imaging along with their limitations and drawbacks is
presented. Various statistical methods based improvements are proposed for through wall
image enhancement. The target subspace estimation concept is used for through wall image
enhancement. The input image is decomposed into different spectral components using singular
value decomposition and then different techniques (conventional, clustering and information
theoretic criterions) are applied to extract target spectral components. Minimum description length
criterion based technique is proposed which successfully estimate target spectral components and
extract single and multiple targets more accurately.
Often in through wall imaging, the boundaries of clutter noise and targets are not sharpey
defined which limit the performance of subspace estimation schemes. This motivates the use of
weighting based addition of different spectral components for improved results. Singular values
and their differences are used in combination with fuzzy logic to calculate weights. K-means
clustering based adjustment of membership functions is proposed. Mamdani and Takagi-Sugeno
inference engines are used for input/output mapping. Later textural features extracted from
spectral components are combined to get further improvements in results.
Beside singular value decomposition based schemes, other statistical methods for through wall
image enhancement are explored. A brief discussion on different statistical methods which can be
combined with proposed techniques is presented. The applications of proposed fuzzy logic based
weight computation in different domains including ground penetrating radar, medical imaging and
image fusion are also explored.