UNSUPERVISED POLINSAR CLASSIFICATION BASED ON OPTIMAL COHERENCE SET

Aiming to solve the misclassification problems of unsupervised polarimetric Wishart clas- sification algorithm based on Freeman decomposition, an unsupervised Polarimetric Synthetic Aper- ture Radar （SAR） Interferotnery （PolInSAR） classification algorithm based on optimal coherence set parameters is studied and proposed. This algorithm uses the result of Freeman decomposition to divide the image into three basic categories including surface scattering, volume scattering, and double-bounce Then, the PolInSAR optimal coherence set parameters are used to finely divide each of the three basic categories into 9 categories, and the whole image is divided into 27 categories. Because both the Freeman decomposition result and optimal coherence set parameters indicate specific scattering characteristics, the whole image is merged into 16 categories based on physical meaning. At last, the Wishart cluster is employed to obtain the final classification result. To preserve the purity of scattering characteristics, pixels with similar scattering characteristics are restricted to be classified with other pixels. The final classification results effectively resolve the misclassification problem, not only the buildings can be effectively distinguished from vegetation in urban areas, but also the road is well distinguished from grass. In this paper, the E-SAR PolInSAR data of German Aerospace Center （DLR） are used to verify the effectiveness of the algorithm.

Cramer-Rao bound and signal-to-noise ratio gain in distributed coherent aperture radar

This paper studies the estimation performance of the coherent processing parameter （CPP）, including time delay differences and phase synchronization errors among different apertures of the distributed coherent aperture radar （DCAR）. Firstly, three architectures of signal processing in the DCAR are introduced. Secondly, the closed-form Cramer-Rao bound （CRB） of the CPP estimation is derived and compared. Then, the closed-form CRB is verified by numerical simulations. Finally, when the next generation radar works in a fully coherent mode, the closed-form signal-to-noise ratio （SNR） gain of the three architectures is presented.

Underwater sonar target imaging via compressed sensing with M sequences

Due to the low sound propagation speed, the tradeoff between high azimuth resolution and wide imaging swath has severely limited the application of sonar underwater target imaging. However, based on compressed sensing(CS) technique, it is feasible to image targets with merely one pulse and thus avoid the above tradeoff. To investigate the possible waveforms for CS-based underwater imaging, the deterministic M sequences widely used in sonar applications are introduced in this paper. By analyzing the compressive matrix constructed from M sequences, the coherence parameter and the restricted isometry property(RIP) of the matrix are derived. Also, the feasibility and advances of M sequence are demonstrated by being compared with the existing Alltop sequence in underwater CS imaging framework. Finally, the results of numerical simulations and a real experiment are provided to reveal the effectiveness of the proposed signal.