This paper presents an autonomous swarm system designed to be an enabling technology for achieving resilience to both partial and complete dropouts of localization of individual vehicles in large teams. The challenge of creating a resilient swarm system across diverse mission types is closely tied to maintaining accurate state awareness, regardless of changing environmental conditions and external threats like jamming and spoofing of primary localization data. Leveraging purely relative measurements and onboard sensor data to ensure accurate state awareness despite intermittent localization failures is extremely important for enhancing security, resilience, and safety of cooperating systems including edge autonomous devices. The first part of this paper focuses a system designed to resist partial localization dropouts of individual robots or even subgroup of robots due to spatial unavailability of localization modalities. This system integrates robust mutual perception mechanisms and shared measurements into the closed-loop primary state estimation pipeline. Such an approach enables the swarm to continue its mission even when localization dropouts occur among a subset of edge drone agents. The second part of the paper examines state-of-the-art techniques aimed at achieving resilience in the event of a global localization dropout, relying exclusively on relative onboard measurements. This method allows the swarm to actively maintain its local constellation, enabling it to continue the mission even after the threat subsides, though at the cost of temporary formation drift. By combining these two approaches, the paper bridges the gap in enhancing the resilience of drone swarm operations, allowing them to adapt dynamically across a wide range of mission types. In our workshop presentation, we will introduce and discuss the description and results of these state-of-the-art distributed state estimation techniques, which significantly strengthen swarm system security against vulnerabilities posed by emerging threats.