The tracking of resident space objects (RSO) by space-based sensors can lead to engagements that result in stressing backgrounds. These backgrounds, including hard earth, earth limb, and zodiacal, pose various difficulties for signal processing algorithms designed to detect and track the target with a minimum of false alarms. Simulated RSO engagements were generated using the Strategic Scene Generator Model and a sensor model to create focal plane scenes. Using this data, the performance of several detection algorithms has been quantified for space, earth limb and cluttered hard earth backgrounds. These algorithms consist of an adaptive spatial filter, a transversal (matched) filters, and a median variance (nonlinear) filter. Signal-to-clutter statistics of the filtered scenes are compared to those of the unfiltered scene. False alarm and detection results are included. Based on these findings, a suggested processing software architectures design is hypothesized.