Decentralized processing algorithms are attractive alternatives to centralized algorithms for target tracking applications in smart sensor networks since they provide the ability to scale, reduce vulnerability, reduce communication and share processing responsibilities among individual nodes. Sharing the processing responsibilities allows parallel processing of raw data at the individual nodes. However, this introduces other difficulties in multi-modal smart sensor networks, such as non- observability of the target state at any individual node and various delays such as varying processing delays, communication delays and signal propagation delays for the different modalities. In this paper, we provide a novel algorithm to determine the initial probability distribution of multiple target states in a decentralized manner. The targets state vector consists of the target positions and velocities on the 2D plane. Our approach can determine the state vector distribution even if the individual sensors alone are not capable of observing it. Our approach can also compensate for varying delays among the assorted modalities. The resulting distribution can be used to initialize various tracking algorithms. Our approach is based on Monte-Carlo methods, where the state distributions are represented as a weighted set of discrete state realizations. A robust weighting strategy is formulated to account for missed detections, clutter and estimation delays. To demonstrate the effectiveness of the algorithm, we simulate a network with direction-of-arrival nodes and range-doppler nodes.