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A promising line of research attempts to bridge the gap between detector and tracker by means of considering jointly optimal parameter settings for both of these subsystems. Along this fruitful path, this thesis study focuses on the problem of detection threshold optimization in a tracker-aware manner so that a feedback from the tracker to the detector is established to maximize the overall system performance. Special emphasis is given to the optimization schemes based on two non-simulation performance prediction (NSPP) methodologies for the probabilistic data association filter (PDAF), namely, the modified Riccati equation (MRE) and the hybrid conditional averaging (HYCA) algorithm. The possible improvements are presented in two domains: Non-maneuvering and maneuvering target tracking. In the first domain, a number of algorithmic and experimental evaluation gaps are identified and newly proposed methods are compared with the existing ones in a unified theoretical and experimental framework. Furthermore, for the MRE based dynamic threshold optimization problem, a closed-form solution is proposed. This solution brings a theoretical lower bound on the operating signal-to-noise ratio (SNR) concerning when the tracking system should be switched to the track before detect (TBD) mode. As the improvements of the second domain, some of the ideas used in the first domain are extended to the maneuvering target tracking case. The primary contribution is made by extending the dynamic optimization schemes applicable to the PDAF to the interacting multiple model probabilistic data association filter (IMM-PDAF). Resulting in an online feedback from the filter to the detector, this extension makes the tracking system robust against track losses under low SNR values.