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The accuracy of radar tracks depends strongly on the variances of the measurements, and those variances are inversely proportional to the signal-to-noise (SNR) produced the hardware and signal processor. The signal processor uses matched filter processing, and the efficiency of that depends on knowledge of the kinematics of the target. In particular, the matched filter performance depends heavily on range rate and range acceleration. Traditionally, the predicted state of the target from the track filter is used for matched filter processing, but the predicted kinematic state can have rather large errors, and those errors result in match filter loss. This loss can be very large for maneuvering (i.e., accelerating) targets. In this paper, an expected-maximization (EM) approach is taken to jointly address signal processing and tracking. The signal processor maximizes the SNR using the predicted state and produces measurements. The state estimator ( e.g., Kalman filter) uses those measurements to produce expected values of the kinematic state (i.e. the nuisance parameters). The signal processor then maximizes the SNR using the new state estimates. This process continues until the maximum likelihood values of the measurements are achieved. In this paper, the Interacting Multiple Model (IMM) estimator is introduced for the tracking function better address sudden maneuvers. The EM-Based approach to join signal processing and tracking are presented along with a discussion of the real-time computing. Monte Carlo simulation results are given to illustrate a 6 dB improvement in SNR and enhanced tracks for a maneuvering target.