Current active satellite maneuver detection techniques can resolve maneuvers as quickly as fifteen minutes post maneuver for large Δv when using angles-only optical tracking. Medium to small magnitude burn detection times range from 6 to 24 h or more. Small magnitude burns may be indistinguishable from natural perturbative effects if passive techniques are employed. Utilizing a photoacoustic signature detection scheme can allow for near real time maneuver detection and spacecraft parameter estimation. We define the acquisition of hypertemporal photometric data as photoacoustic sensing because the data can be played back as an acoustic signal. Studying the operational frequency spectra, profile, and aural perception of an active satellite event such as a thruster ignition or any subsystem operation can provide unique signature identifiers that support resident space object characterization efforts. A thruster ignition induces vibrations in a satellite body which can modulate reflected sunlight. If the reflected photon flux is sampled at a sufficient rate, the change in light intensity due to the propulsive event can be detected. Sensing vibrational mode changes allows for a direct timestamp of thruster ignition and shut-off events and thus makes possible the near real time estimation of spacecraft Δv and maneuver type if coupled with active observations immediately post maneuver. This research also investigates the estimation of other impulse related spacecraft parameters such as mass, specific impulse, exhaust velocity, and mass flow rate using impulse-momentum and work-energy methods. Experimental results to date have not yet demonstrated an operator-correlated detection of a propulsive event; however, the application of photoacoustic sensing has exhibited characteristics unique to hypertemporal photometry that are discussed alongside potential improvements to increase the probability of active satellite event detection. Simulations herein suggest that large, potentially destructive modal displacements are required for optical sensor detection and thus more comprehensive vibration modeling and signal-to-noise ratio improvements should be explored.
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