Sensors are getting smaller, inexpensive and sophisticated, with an increased availability. Compared to 25 years ago, an object tracking system now can easily achieve twice the accuracy, a much larger coverage and fault tolerance, without any significant changes in the overall cost. This is possible by simply employing more than just one sensor and processing measurements from individual sensors sequentially (or even in a batch form). %This is the centralized scheme of multi-sensor target tracking wherein the sensors send their individual detections to a central facility, where tracking related tasks such as data association, filtering, and track management etc. are performed. This is also perhaps the simplest solution for a multi-sensor approach and also optimal in the sense of minimum mean square error (MMSE) among all other multi-sensor scenario. In sophisticated sensors, the number of detections can reach thousands in a single frame. The communication and computation load for gathering all such detections at the fusion center will hamper the system's performance while also being vulnerable to faults. A better solution is a distributed architecture wherein the individual sensors are equipped with processing capabilities such that they can detect measurements, extract clutter, form tracks and transmit them to the fusion center. The fusion center now fuses tracks instead of measurements, due to which this scheme is commonly termed track-level fusion. In addition to sub-optimality, the track-level fusion suffers from a very coarse problem, which occurs due to correlations between the tracks to be fused. Often, in realistic scenarios, the cross-correlations are unknown, without any means to calculate them. Thus, fusion cannot be performed using traditional methods unless extra information is transmitted from the fusion center. This thesis proposes a novel and generalized method of fusing any two probability density functions (pdf) such that a positive cross-correlation exists between them. In modern tracking systems, the tracks are essentially pdfs and not necessarily Gaussian. We propose harmonic mean density based fusion and prove that it obeys all the necessary requirements of being a viable fusion mechanism. We show that fusion in this case is a classical example of agreement between the fused and participating densities based on average $\chi^2$ divergence. Compared to other such fusion techniques in the literature, the HMD performs exceptionally well. Transmitting covariance matrices in distributed architecture is not always possible in cases for e.g. tactical and automotive systems. Fusion of tracks without the knowledge of uncertainty is another problem discussed in the thesis. We propose a novel technique for local covariance reconstruction at the fusion center with the knowledge of estimates and a vector of times when update has occurred at local sensor node. It has been shown on a realistic scenario that the reconstructed covariance converges to the actual covariance, in the sense of Frobenius norm, making fusion without covariance, possible. / Thesis / Doctor of Philosophy (PhD)