Typically, search research papers assume that target acquisition is described by an exponential distribution. We investigate when this assumption is valid. It is obvious that two people are more effective than one person at finding a target, but how can that be quantified? The network imaging sensor (NIS) and time-dependent search parameter (TDSP) models quantify how much more effective multiple observers are at finding a target than a single individual for a wide variety of scenarios. We reference and summarize evidence supporting the NIS and TDSP models and demonstrate how NIS model results can be expressed in terms of a reduced hyperexponential distribution for scenarios where observer and target are stationary. Target acquisition probabilities are determined by analysis and confirmed by computer simulations and perception experiments. Search by multiple stationary observers looking for a stationary target is described by the hyperexponential distribution. Stationary scenarios with multiple observers are more accurately modeled by hyperexponential rather than exponential distributions. Hyperexponential distributions are an example of phase-type distributions used in queuing and in the performance evaluation of computer networks and systems. The observation that search, queuing, and computer networks share phase-type distributions facilitates cross fertilization between these fields.
Reconnaissance from an unmanned aerial systems (UAS) is often done using video presentation. An alternate method is
Serial Visual Presentation (SVP). In SVP, a static image remains in view until replaced by a new image at a rate
equivalent to the live video. Mardell et al. have shown, in a forested environment, that a higher fraction of targets
(people lost in the forest), are found with SVP than with video presentation. Here Mardell’s experiment is repeated for
military targets in forested terrain at a fixed altitude. We too find a higher fraction of targets are found using SVP rather
than video presentation. Typically it takes five seconds to cover a video field of view and at 30 frames per second. This
implies that, for scenes where the target is not moving, 150 video images have nearly identical information (from a
reconnaissance point of view) as a single SVP image. This is highly significant since transmission bandwidth is a
limiting factor for most UASs. Finding targets in video or in SVP is an arduous task. For that reason we also compare
aided target detection performance (Aided SVP) and unaided target detection performance on SVP images.