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Many current radar-based human detection systems employ some type of Doppler or Fourier-based processing, followed by spectrogram and gait analysis to classify detected targets. However, Fourier-based techniques inherently assume a linear variation in target phase over the aperture, whereas human targets have a highly nonlinear phase history. This mismatch leads to significant loss in SNR and integration gain. In this paper, an Enhanced Optimized Non-Linear Phase (EnONLP) detector is proposed that employs a dictionary to store possible target returns generated from the human model for each combination of parameter values. An orthogonal matching pursuit algorithm is used to compute a sparse approximation to the radar return that is optimal in the least squares sense. Performance of the EnONLP algorithm is compared to that of a parameter-estimation based algorithm and conventional, fully-adaptive STAP.