A model and simulation to predict the performance of angle-angle-range 3D flash LADAR imaging sensor systems

BAE SYSTEMS reports on a program to develop a high-fidelity model and simulation to predict the performance of angle-angle-range 3D flash LADAR Imaging Sensor systems. 3D Flash LADAR is the latest evolution of laser radar systems and provides unique capability in its ability to provide high-resolution LADAR imagery upon a single laser pulse; rather than constructing an image from multiple pulses as with conventional scanning LADAR systems. However, accurate methods to model and simulate performance from these 3D LADAR systems have been lacking, relying upon either single pixel LADAR performance or extrapolating from passive detection FPA performance. The model and simulation developed and reported here is expressly for 3D angle-angle-range imaging LADAR systems. To represent an accurate "real world" type environment, this model and simulation accounts for: 1) laser pulse shape; 2) detector array size; 3) atmospheric transmission; 4) atmospheric backscatter; 5) atmospheric turbulence; 6) obscurants, and; 7) obscurant path length. The angle-angle-range 3D flash LADAR model and simulation accounts for all pixels in the detector array by modeling and accounting for the non-uniformity of each individual pixel in the array. Here, noise sources are modeled based upon their pixel-to-pixel statistical variation. A cumulative probability function is determined by integrating the normal distribution with respect to detector gain, and, for each pixel, a random number is compared with the cumulative probability function resulting in a different gain for each pixel within the array. In this manner very accurate performance is determined pixel-by-pixel. Model outputs are in the form of 3D images of the far-field distribution across the array as intercepted by the target, gain distribution, power distribution, average signal-to-noise, and probability of detection across the array. Other outputs include power distribution from a target, signal-to-noise vs. range, probability of target detection and identification, and NEP vs. gain.