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Global navigation satellite systems (GNSS) are insufficient for reliable anytime, anywhere navigation, particularly indoors, in deep urban canyons, and in environments under malicious attacks (e.g., jamming and spoofing). Motivated by the plenitude of ambient radio frequency signals of opportunity (SOPs), such as cellular, HDTV, AM/FM, and WiFi, a new navigation paradigm, termed opportunistic navigation (OpNav), is emerging. OpNav receivers continuously search for SOPs from which to draw position and timing information, employing on-the-fly signal characterization as necessary. In collaborative OpNav (COpNav), multiple receivers share information to construct and continuously refine a global signal landscape within which they localize themselves in space and time. This dissertation studied two fundamental COpNav problems. First, the observability and estimability of COpNav environments were analyzed. Second, several motion planning strategies for optimal information gathering were synthesized, including greedy strategies, receding horizon trajectory optimization, and collaborative signal landscape mapping architectures. The theoretical conclusions were validated numerically and experimentally.
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