Sensor array signal processing has emerged a key technology for future radar, wireless communication, sonar, and microphone array systems. Sensor array processing methods are commonly used in multi-sensor systems to estimate signal parameters (such as source directions-of-arrival and waveforms), to extract signals-of-interest received on the background of multiple interferers and sensor noise, and to transmit signals to multiple locations in a spatially selective way. Current sensor array processing techniques are known to degrade severely if some of the exploited assumptions on the environment, sources, or antenna array become wrong or imprecise. In particular, the existing signal parameter estimation and beamforming methods are known to be very sensitive to any type of array manifold or multi-antenna channel mismodeling which may be caused by environmental non-stationarities, signal fading and scattering effects, array shape distortions, imperfect array calibration, unknown ambient colored noise, etc. As a result, advanced sensor array processing techniques can suffer a severe performance degradation in situations with even rather slight mismatches between the actual and presumed array responses. Therefore, robust sensor array processing techniques are currently of great demand. The main objective of the proposed research will be to develop new robust and computationally efficient sensor array signal processing methods for signal parameter estimation and receive and transmit adaptive beamforming in complicated environments with imperfectly known array and propagation channel parameters. The robust adaptive array processing and signal parameter estimation techniques developed during this project will be based on an explicit modeling of uncertainties in the array response and robust performance optimization. The proposed methods will provide substantial improvements of the robustness and computational complexity relative to the current array processing techniques.