3 research outputs found
Improved DORT for breast cancer detection in low contrast scenarios
© 2015 The Institute of Electronics, Information and Comm. Microwave imaging performance deteriorates with increasing clutter and heterogeneity in the imaging medium. Breast cancer detection becomes increasingly challenging with increasing breast density. Decomposition of the time reversal operator (DORT) uses signal subspace of the multistatic matrix which is perturbed in highly heterogeneous medium. To overcome the problem we propose coherent processing in frequency domain prior to imaging operation. Coherent DORT (C-DORT) provides robust imaging performance compared to conventional non-coherent DORT in cluttered medium as evident from the imaging results obtain using anatomically realistic numerical breast phantoms
A comparative study of coherent time reversal minimum variance beamformers for breast cancer detection
© 2015 EurAAP. In this study we consider coherent processing for time reversal microwave imaging for breast cancer detection. We derive coherent time reversal standard Capon beamformer (C-TR-SCB) and coherent time reversal robust Capon beamformer (C-TR-RCB) and compare their imaging performances for breast cancer detection in anatomically realistic heterogeneous 3-D breast phantoms
Space Time MUSIC: Consistent Signal Subspace Estimation for Wide-band Sensor Arrays
Wide-band Direction of Arrival (DOA) estimation with sensor arrays is an
essential task in sonar, radar, acoustics, biomedical and multimedia
applications. Many state of the art wide-band DOA estimators coherently process
frequency binned array outputs by approximate Maximum Likelihood, Weighted
Subspace Fitting or focusing techniques. This paper shows that bin signals
obtained by filter-bank approaches do not obey the finite rank narrow-band
array model, because spectral leakage and the change of the array response with
frequency within the bin create \emph{ghost sources} dependent on the
particular realization of the source process. Therefore, existing DOA
estimators based on binning cannot claim consistency even with the perfect
knowledge of the array response. In this work, a more realistic array model
with a finite length of the sensor impulse responses is assumed, which still
has finite rank under a space-time formulation. It is shown that signal
subspaces at arbitrary frequencies can be consistently recovered under mild
conditions by applying MUSIC-type (ST-MUSIC) estimators to the dominant
eigenvectors of the wide-band space-time sensor cross-correlation matrix. A
novel Maximum Likelihood based ST-MUSIC subspace estimate is developed in order
to recover consistency. The number of sources active at each frequency are
estimated by Information Theoretic Criteria. The sample ST-MUSIC subspaces can
be fed to any subspace fitting DOA estimator at single or multiple frequencies.
Simulations confirm that the new technique clearly outperforms binning
approaches at sufficiently high signal to noise ratio, when model mismatches
exceed the noise floor.Comment: 15 pages, 10 figures. Accepted in a revised form by the IEEE Trans.
on Signal Processing on 12 February 1918. @IEEE201