Data fusion of nonlinear measurement data in the presence of correlated sensor-to-sensor errors is examined. The scenario involves three spatiallydistributed sensors making three noisy angle-ofarrival measurements on a signal emitted by a source whose position is to be estimated. The noisy angleof- arrival measurements from two of the sensors are triangulated to form a noisy position measurement in two dimensions. A second pair of sensor noisy angleof- arrival measurements are also triangulated to form a second noisy position measurement. Both of these noisy position measurements are nonlinear functions of the noisy angle-of-arrival measurements. Since there are three sensors SI, S2, and S3, sensor S2 is common to both triangulation process, causing a non-zero cross-correlation across both noisy nonlinear position measurements. Since the position measurements are nonlinear functions of the angle-ofarrival measurements, we must use a first-order approximation to the covariance matrix for each measurement vector. The statistics governing the errors on these angle measurements come from a variety of distributions, namely the uniform, sawtooth, and triangular distributions. The optimum fusion algorithm applied to the distributed measurements forms a linear operation on the measurement vectors. Since the measurements are nonlinear functions of the parameters, an exact calculation of the covariance matrix in closed form is not possible because of the intractable nature of the mathematics involved. Consequently, these conditions give sub-optimum conditions for the algorithm. However it is found that the trace of the error covariance matrix of the fused measurement is less than the trace of the error covariance matrix associated with each individual measurement vector. Finally, a mathematical high-order approximation to the covariance matrix is performed. The impact of these high-order terms is examined through simulations.