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Humans are difficult targets to detect because they have small radar cross sections (RCS) and move at low velocities. Consequently, they are masked by Doppler spread ground clutter generated by the radar bearing platform motion. Furthermore, conventional radar-based human detection systems employ some type of linear-phase matched filtering, whereas most human targets generate a highly nonlinear phase history. This work proposes an enhanced, optimized, nonlinear phase (EnONLP) matched filter that exploits knowledge of human gait to improve the radar detection performance of human targets. A parametric model of the expected human response is derived for multi-channel radar systems and used to generate a dictionary of human returns for a range of possible parameter variations. The best linear combination of projections in this dictionary is computed via orthogonal matching pursuit (OMP) to detect and extract features for multiple targets. Performance of the proposed EnONLP method is compared with that of traditional space-time adaptive processing (STAP) and a previously derived parameter estimation-based ONLP detector. Results show that EnONLP exhibits a detection probability of about 0.8 for a clutter-to-noise (CNR) ratio of 20 dB and input signal-to-noise ratio (SNR) of 0 dB, while ONLP yields a 0.3 and STAP yields a 0.18 probability of detection for the same false alarm rate.