Non-MSE Wavelet-Based Data Compression for Emitter Location

The location of an emitter is estimated by intercepting its signal and sharing the data among several platforms to measure the time-difference-of-arrival (TDOA) and the frequency-difference-of-arrival (FDOA). Doing this in a timely fashion requires effective data compression. A common compression approach is to use a rate-distortion criterion where distortion is taken to be the mean-square error (MSE) between the original and compressed versions of the signal. However, in this paper we show that this MSE-only approach is inappropriate for TDOA/FDOA estimation and then define a more appropriate, non-MSE distortion measure. This measure is based on the fact that in addition to the dependence on MSE, the TDOA accuracy also depends inversely on the signal’s RMS (or Gabor) bandwidth and the FDOA accuracy also depends inversely on the signal’s RMS (or Gabor) duration. We discuss how the wavelet transform is a natural choice to exploit this non-MSE criterion. These ideas are shown to be natural generalizations of our previously presented results showing how to determine the correct balance between quantization and decimation. We develop a MSE-based wavelet method and then incorporate the non-MSE error criterion. Simulations show the wavelet method provides significant compression ratios with negligible accuracy reduction. We also make comparisons to methods that don’t exploit time-frequency structure and see that the wavelet methods far out-perform them.