As the future progresses for space exploration endeavors, spacecraft that are capable of autonomously determining their position and velocity will provide clear navigation advances to mission operations. Thus, new techniques for determining spacecraft navigation solutions using celestial gamma-ray sources have been developed. Most of these sources offer detectable, bright, high-energy events that provide well-defined characteristics conducive to accurate time alignment among spatially separated spacecraft. Using assemblages of photons from distant gamma-ray bursts, relative range between two spacecraft can be accurately computed along the direction to each burst's source based upon the difference in arrival time of the burst emission at each spacecraft's location. Correlation methods used to time-align the high-energy burst profiles are provided. A simulation of the newly devised navigation algorithms has been developed to assess the system's potential performance. Using predicted observation capabilities for this system, the analysis demonstrates position uncertainties comparable to the NASA Deep Space Network for deep-space trajectories. Nomenclature c = speed of light, m∕s f = nonlinear state vector function h = nonlinear simulated state vector function H = measurement matrix of partial derivatives with respect to states fi; j; kg = spacecraft coordinate system unit axis directionŝ n = line-of-sight vector, radians r SC = three-dimensional spacecraft position, m r Base = three-dimensional base station position, m S = fluence, erg∕cm 2 ∕T 90 t 0 = emission trigger time, s T 90 = burst emission time from 5 to 95% of total photon counts, s ν = simulated measurement noise vector v RSC = remote spacecraft velocity, m∕s x = extended Kalman filter (EKF) true state vector _ xt = nonlinear spacecraft orbital dynamics x = EKF estimated state vector y = observation (filter measurement) z = EKF measurement difference zt = measurement residual Δr = burst position offset, m Δt = burst arrival time offset, s δx = state vector errors ηt = measurement noise vector σ pos 0 = EKF initial position covariance estimate, m σ vel 0 = EKF initial velocity covariance estimate, m∕s

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