Sensor Network and Data Fusion In sensor network tracking systems, bandwidth and power are guarded resources. Most sensor network tracking systems fall in the two categories: centralized and distributed, in which either measurements or local tracks are shared. (a) For the centralized systems, we proposed an intelligent measurement quantization method and bandwidth requirement of measurement sharing can be considerably cut. We also discussed several implemental issues, such as non-Gaussian distribution noise due to quantization, out-of-sequence measurement due to communication delay, and etc. We proposed solutions to these issues in the framework of a compander/particle-filter combination, and we showed that quite good performance is achievable with only 2-3 bits per dimension per observation. (b) Many distributed multi-sensor tracking systems are based on some form of track fusion, in which local track estimates and their associated covariances are shared among sensors. Communication load is a significant concern. We proposed an architecture for low bandwidth track fusion. The scheme involves intelligent scalar and vector quantization of the local state estimates and of the associated estimation error covariance matrices. Simulation studies indicate that the communication saving can be quite significant, with only minor degradation of track accuracy.