The case for like-sensor predetection fusion

There has been a great deal of theoretical study into decentralized detection networks composed of similar (often identical), independent sensors, and this has produced a number of satisfying theoretical results. At this point it is perhaps worth asking whether or not there is a great deal of point to such study-certainly two sensors can provide twice the illumination of one, but what does this really translate to in terms of performance? We take as our metric the ground area covered with a specified Neyman-Pearson detection performance. To be fair, the comparison will be of a multisensor network to a single-sensor system where both have the same aggregate transmitter power. The situations examined are by no means exhaustive but are, we believe, representative. Is there a case? The answer, as might be expected, is “sometimes.” When the statistical situation is well behaved there is very little benefit to a fused system; however, when the environment is hostile the gains can be significant. We see, depending on the situation, gains from colocation, gains from separation, optimal gains from operation at a “fusion range,” and sometimes no gains at all