Sparse nonlinear optimization for signal processing and communications

This dissertation proposes three classes of new sparse nonlinear optimization algorithms for network echo cancellation (NEC), 3-D synthetic aperture radar (SAR) image reconstruction, and adaptive turbo equalization in multiple-input multiple-output (MIMO) underwater acoustic (UWA) communications, respectively. For NEC, the proposed two proportionate affine projection sign algorithms (APSAs) utilize the sparse nature of the network impulse response (NIR). Benefiting from the characteristics of l₁-norm optimization, affine projection, and proportionate matrix, the new algorithms are more robust to impulsive interferences and colored input than the conventional adaptive algorithms. For 3-D SAR image reconstruction, the proposed two compressed sensing (CS) approaches exploit the sparse nature of the SAR holographic image. Combining CS with the range migration algorithms (RMAs), these approaches can decrease the load of data acquisition while recovering satisfactory 3-D SAR image through l₁-norm optimization. For MIMO UWA communications, a robust iterative channel estimation based minimum mean-square-error (MMSE) turbo equalizer is proposed for large MIMO detection. The MIMO channel estimation is performed jointly with the MMSE equalizer and the maximum a posteriori probability (MAP) decoder. The proposed MIMO detection scheme has been tested by experimental data and proved to be robust against tough MIMO channels. --Abstract, page iv

The development of near field probing systems for EMC near field visualization and EMI source localization

The objectives of this research are to visualize the frequency dependent electromagnetic field distribution for electromagnetic compatibility (EMC) applications and the radiating source reconstruction on complex shaped electronic systems. This is achieved by combining near field probing with a system for automatically recording the probe position and orientation. Due to the complexity of the shape of the electronic systems of interest, and for utilizing the expertise of the user, the probe will be moved manually not robotically. Concurrently, the local near field will be recorded, associated with the location and displayed at near real time on the captured 3D geometry as a field strength map for EMC applications and, for source reconstruction, a reconstructed image showing the far field radiating sources. --Abstract, page iii

Emission source microscopy applications on EMI source localization and EMI mitigation with lossy materials

In Section 1, the emission source microscopy (ESM) methodology will be introduced and used to identify the sources of radiation on different DUTs. As the new technology generation, the integration density and the operating speed of integrated circuits have been increasing steadily. However, root cause diagnostics to locate the source of EMI radiation is more problematic in the complex system. The ESM technique provides a powerful tool to detect and characterize the active sources of radiation. The amplitude and phase of fields are measured on a plane away from the DUT, and this measurement can get rid of the evanescent waves influence in the near field. The ESM algorithm is then applied to propagate the fields back to the source plane and to localize the sources of radiation. The ESM method is used on different DUTs at different frequencies to evaluate the source identification performance. The results show that the proposed method is capable of detecting multiple active sources on a complex system. In section 2, the possibility of building channel emulators by utilizing fused deposition modeling (FDM) 3D printing technology is investigated. The FDM 3D printing provides a rapid and economic method to produce parts with different shapes. An optimizing algorithm was developed for obtaining the printing pattern and loss profile. Those parts with different dielectric constants and loss tangents will be printed on a low loss transmission line to modify its transmission or reflection. As a result, different channel emulators can be built to emulate the S-parameter and eye diagrams of a target channel with the advantage of avoiding complicated electronic components --Abstract, page iii

Compressive sensing for 3D microwave imaging systems

Compressed sensing (CS) image reconstruction techniques are developed and experimentally implemented for wideband microwave synthetic aperture radar (SAR) imaging systems with applications to nondestructive testing and evaluation. These techniques significantly reduce the number of spatial measurement points and, consequently, the acquisition time by sampling at a level lower than the Nyquist-Shannon rate. Benefiting from a reduced number of samples, this work successfully implemented two scanning procedures: the nonuniform raster and the optimum path. Three CS reconstruction approaches are also proposed for the wideband microwave SAR-based imaging systems. The first approach reconstructs a full-set of raw data from undersampled measurements via L1-norm optimization and consequently applies 3D forward SAR on the reconstructed raw data. The second proposed approach employs forward SAR and reverse SAR (R-SAR) transforms in each L1-norm optimization iteration reconstructing images directly. This dissertation proposes a simple, elegant truncation repair method to combat the truncation error which is a critical obstacle to the convergence of the CS iterative algorithm. The third proposed CS reconstruction algorithm is the adaptive basis selection (ABS) compressed sensing. Rather than a fixed sparsifying basis, the proposed ABS method adaptively selects the best basis from a set of bases in each iteration of the L1-norm optimization according to a proposed decision metric that is derived from the sparsity of the image and the coherence between the measurement and sparsifying matrices. The results of several experiments indicate that the proposed algorithms recover 2D and 3D SAR images with only 20% of the spatial points and reduce the acquisition time by up to 66% of that of conventional methods while maintaining or improving the quality of the SAR images --Abstract, page iv