Novel Approaches to Overloaded Array Processing

An antenna array is overloaded when the number of cochannel signals in its operating environment exceeds the number of elements. Conventional space-time array processing for narrow-band signals fails in overloaded environments. Overloaded array processing (OLAP) is most difficult when signals impinging on the array are near equal power, have tight excess bandwidth, and are of identical signal type. Despite the failure of conventional beamforming in such environments, OLAP becomes possible when a receiver exploits additional signal properties such as the finite-alphabet property and signal excess-bandwidth. This thesis proposes three approaches to signal extraction in overloaded environments, each providing a different tradeoff in performance and complexity. The first receiver architecture extracts signals from an overloaded environment through the use of MMSE interference rejection filtering embedded in a successive interference cancellation (SIC) architecture. The second receiver architecture enhances signal extraction performance by embedding a stronger interference rejection receiver, the reduced-state maximum aposteriori probability (RS-MAP) algorithm in a similar SIC architecture. The third receiver fine-tunes the performance of spatially reduced search joint detection (SRSJD) with the application of an energy focusing transform (EFT), a complexity reducing front-end linear pre-processor. A new type of EFT, the Energy Focusing Unitary Relaxed Transform (EFURT) is developed. This transform facilitates a continuous tradeoff between noise-enhancement and error-propagation in an SRSJD framework. EFURT is used to study the role of this tradeoff for SRSJD receivers in a variety of signal environments. It is found that for the environments studied in this thesis, SRSJD enjoys an aggressive reduction in interference at the expense of possible noise-enhancement.