In Part I of this two-part series, we described the adaptive double-gated filter (DGF), a nonlinear CS detector that is sensitive to target shape and statistical properties. In particular, we derived the DGFs parameters from knowledge of target shape, size, and statistics, as well as background statistics. Several variants of the DGF were presented different for target recognition regimes (e.g., smooth targets against rough backgrounds, and vice versa). An additional variant of the DGF, known as the adaptive triple-gated-filter (TGF) was presented which aids in target registration. In this paper, we analyze the errors inherent in the DGF and TGF. Our analyses emphasize the performance of the DGF in limiting conditions, including high noise and poorly defined targets. Additionally, we discuss implementational optimization of the DGFs performance and time complexity. Examples of DGFs application to a database of field images presented, with discussion of results stated in terms of probability of hits and rate of false alarms.