Geometry-adaptive data compression for TDOA/FDOA location [wireless sensor network applications]

The location of an emitting target is estimated by intercepting its emitted signal and sharing it among several sensors to measure the time-difference-of-arrival (TDOA) and the frequency-difference-of-arrival (FDOA). Doing this in a timely and energy efficient fashion, which is especially important for wireless sensor network applications, requires effective data compression. Since the commonly used MSE distortion measure is only weakly related to optimal TDOA/FDOA estimation, in this paper, we derive a new class of non-MSE distortion measures for TDOA/FDOA estimation using the concept of Fisher information. We then use these new distortion measures to compress the data using a wavelet packet transform and show that it improves TDOA/FDOA estimation accuracies relative to using the MSE-based compression. Finally, a scheme of applying our algorithms in a wireless sensor network is proposed, and energy efficiency and accuracy enhancement of the proposed scheme over that of a traditional scheme using MSE is shown through simulations.