Autonomous platforms are becoming ubiquitous in society, including UAVs, Roombas, and self-driving cars. With the increase in prevalence of autonomous platforms comes an increase in the threat of attacks against these platforms. These attacks can range from direct hacking to remotely take control of the platforms themselves , to attacks involving manipulation or deception such as spoofing or fooling sensor inputs [2, 3]. Ensuring autonomous systems are robust and resilient (R2) against these attacks will become an important challenge to overcome if they are to be trusted and widely adopted. This paper addresses the need to quantitatively define robustness and resilience against manipulation and deceptive attacks which are inherently harder to detect. We define a set of robust estimation metrics that are mathematically rigorous, can be applied to multiple algorithm use cases, and are easy to interpret. Since many of these functions are processed over time, the primary focus will be on process-based metrics. These metrics can be adapted over time by responding and reconfiguring at system runtime. This paper will: 1) provide background information on previous work in this area, including adversarial machine learning, robotics control, and engineering design. 2) Present the metrics and explain how to address our unique problem. 3) Apply these metrics to three different autonomy applications: target tracking, autonomous control, and automatic target recognition. 4) Discuss some additional caveats and potential areas for future work.