This dissertation considers radar detection and tracking of weak fluctuating targets using dynamic programming (DP) based track-before-detect (TBD). TBD combines target detection and tracking by integrating data over consecutive scans before making a decision on the presence of a target. A novel algorithm is proposed which employs order statistics in dynamic programming based TBD (OS-DP-TBD) to detect weak fluctuating targets. The well-known Swerling type 0, 1 and 3 targets are considered with non-Gaussian distributed clutter and complex Gaussian noise. The clutter is modeled using the Weibull, K and G0 distributions. The proposed algorithm is shown to provide better performance than well-known techniques in the literature. In addition, a novel expanding window multiframe (EW-TBD) technique is presented to improve the detection performance with reasonable computational complexity compared to batch processing. It is shown that EW-TBD has lower complexity than existing multiframe processing techniques. Simulation results are presented which confirm the superiority of the proposed expanding window technique in detecting targets even when they are not present in every scan in the window. Further, the throughput of the proposed technique is higher than with batch processing. Depending on the range and azimuth resolution of the radar system, the target may appear as a point in some radar systems and there will be target energy spillover in other systems. This dissertation considers both extended targets with different energy spillover levels and point targets. Simulation results are presented which confirm the superiority of the proposed algorithm in both cases. / Graduate