We consider sparse signals embedded in additive white noise. We study parametrically optimal as well as tree-search sub-optimal signal detection policies. As a special case, we consider a constant signal and Gaussian noise, with and without data outliers present. In the presence of outliers, we study outlier resistant robust detection techniques. We compare the studied policies in terms of error performance, complexity and resistance to outliers.
 E. J. Candes, J. Romberg and T. Tao, “Robust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete Frequency Information,” IEEE Transactions on Information Theory, Vol. 52, No. 2, 2006, pp. 489-509. doi:10.1109/TIT.2005.862083
 J. A. Tropp and A. C. Gilbert, “Signal Recovery from Random Measurements via Orthogonal Matching Pursuit,” IEEE Transactions on Information Theory, Vol. 53, No. 2, 2007, pp. 4655-4666. doi:10.1109/TIT.2007.909108
 A. T. Burrell and P. Papantoni-Kazakos, “Random Access Algorithms in Packet Networks: A Review of Three Research Decades,” International Journal of Communications Network and System Sciences (IJCNS), Vol. 5, No. 10, 2012, pp. 691-707. doi:10.4236/ijcns.2012.510072
 A. T. Burrell and P. Papantoni-Kazakos, “Extended Sequential Algorithms for Detecting Changes in Acting Stochastic Processes,” IEEE Transactions on Systems, Man, and Cybernetics, Vol. 28, No. 5, 1998, pp. 703-710. doi:10.1109/3468.709621
 A. T. Burrell and P. Papantoni-Kazakos, “Robust Sequential Algorithms for the Detection of Changes in Data Generating Processes,” Journal of Intelligent and Robotic Systems, Vol. 60, No. 1, 2010, pp. 3-17.