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The Complex Ambiguity Function (CAF) used in emitter location measurement is a 2-dimensional complex-valued function of time-difference-of-arrival (TDOA) and frequency-difference-of-arrival (FDOA). In classical TDOA/FDOA systems, pairs of sensors share data (using compression) to compute the CAF, which is then used to estimate the TDOA/FDOA for each pair; the sets of TDOA/FDOA measurements are then transmitted to a common site where they are fused into an emitter location. However, in some recently published methods for improved emitter location methods, it has been proposed that after each pair of sensors computes the CAF it is the entire CAFs that should be shared rather than the extracted TDOA/FDOA estimates. This leads to a need for methods to compress the CAFs. Because a CAF is a 2-D functions it can be thought of as a form of image – albeit, a complex-valued image. We apply and appropriately modify the Embedded Zerotree Wavelet (EZW) to compress the Ambiguity Function. Several techniques are analyzed to exploit the correlation between the imaginary part and real part of Ambiguity Function and comparisons are made between the approaches. The impact of such compression on the overall location accuracy is assessed via simulations.