Decimation vs

The location of an electromagnetic emitter is commonly estimated by intercepting its signal and then sharing the data among several platforms. Doing this in a timely fashion requires effective data compression. Previous data compression efforts have focused on minimizing the mean-square error (MSE) due to compression. However, this criterion is likely to fall short because it fails to exploit how the signal's structure impacts the parameter estimates. Because TDOA accuracy depends on the signal's RMS bandwidth, compression techniques that can significantly reduce the amount of data while negligibly impacting the RMS bandwidth have great potential. We show that it is possible to exploit this idea by balancing the impacts of simple filtering/decimation and quantization and derive a criterion that determines an optimal balance between the amount of decimation and the level of quantization. This criterion is then used to show that by using a combination of decimation and quantization it is possible to meet requirements on data transfer time that can't be met through quantization alone. Furthermore, when quantization-alone approaches can meet the data transfer time requirement, we demonstrate that the decimation/quantization approach can lead to better TDOA accuracies. Rate-distortion curves are plotted to show the effectiveness of the approach.