Greedy motion planning strategies to enhance situational awareness in an opportunistic navigation (OpNav) environment are considered. An OpNav environment can be thought of as a radio frequency signal landscape within which a receiver locates itself in time and space by extracting information from ambient signals of opportunity (SOPs). The receiver is assumed to draw only pseudorange observations from the SOPs. The following problem is considered. A receiver with no a priori knowledge about its own initial states nor the initial states of multiple SOPs, except for one, is dropped in an OpNav environment. Assuming that the receiver can prescribe its maneuvers, what greedy (i.e., one-step look-ahead) motion planning strategy should the receiver adopt so to optimally build a high-fidelity signal landscape map of the environment while simultaneously localizing itself within this map in time and space with high accuracy? Several information-based and innovation-based motion planning strategies are studied. It is shown that with proper reformulation, the innovation-based strategies can be cast as tractable convex programs, the solution of which is computationally efficient. Simulation results are presented comparing the various strategies and illustrating the improvements gained from adopting the proposed strategies over random and predefined receiver trajectories.