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Recently, the use of micro-Doppler radar signatures for target classification has become an area of focus, in particular for the case of dynamic targets where many components are interacting over time. To fully exploit the signature information, individual scattering centers may be automatically extracted and associated over the full target observation. The availability of ultrafine radar range resolution, or micro-range resolution, aids this process immensely. This paper proposes one such algorithm. The proposed method uses the well-known nonlinear least squares (NLS) and expectation-maximization (EM) algorithms. As shown, leveraging fine range and Doppler resolution allows human signatures to be decomposed into the responses of constituent body parts. The algorithm is experimentally validated against a number of measured human-radar data sets.