Brian Portock

Mars Exploration Rovers orbit determination filter strategy.

§†† ‡‡ §§ §§ , The successful delivery of the Mars Exploration Rover (MER) landers to well within the boundaries of their surface target areas in January of 2004 was the culmination of years of orbit determination analysis. The process began with a careful consideration of the filter parameters used for pre-launch covariance studies, and continued with the refinement of the filter after launch based on operational experience. At the same time, tools were developed to run a plethora of variations around the nominal filter and analyze the results in ways that had never been previously attempted for an interplanetary mission. In addition to achieving sub-kilometer Mars-relative orbit determination knowledge, the filter strategy and process detected unexpected error sources, while at the same time proving robust by indicating the correct solution. Consequently, MER orbit determination set a new standard for interplanetary navigation. Nomenclature

Navigation Challenges of the Mars Phoenix Lander Mission

The Mars Phoenix Lander mission was launched on August 4th, 2007. To land safely at the desired landing location on the Mars surface, the spacecraft trajectory had to be controlled to a set of stringent atmospheric entry and landing conditions. The landing location needed to be controlled to an elliptical area with dimensions of 100km by 20km. The two corresponding critical components of the atmospheric entry conditions are the entry flight path angle (target: -13.0 deg +/-0.21 deg) and the entry time (within +/-30 seconds). The purpose of this paper is to describe the navigation strategies used to overcome the challenges posed during spacecraft operations, which included an attitude control thruster calibration campaign, a trajectory control strategy, and a trajectory reconstruction strategy. Overcoming the navigation challenges resulted in final Mars atmospheric entry conditions just 0.007 deg off in entry flight path angle and 14.9 sec early in entry time. These entry dispersions in addition to the entry, descent, and landing trajectory dispersion through the atmosphere, lead to a final landing location just 7 km away from the desired landing target.

Mars Exploration Rover cruise orbit determination

The Mars Exploration Rover project consisted of two missions (MER-A: spirit rover and MER-B: opportunity rover) that launched spacecraft on June 10, 2003, and July 8, 2003, respectively. The spacecraft arrived at Mars approximately seven months later on January 4, 2004, and January 24, 2004. These spacecraft needed to be precisely navigated to a Mars atmospheric entry flight path angle of -11.5 deg +/-0.12 deg (3(sigma)) for MER-A and +/-0.14 deg (3(sigma)) for MER-B in order to satisfy the landing site delivery requirements. The orbit determination task of the navigation team needed to accurately determine the trajectory of the spacecraft, predict the trajectory to Mars atmospheric entry, and account for all possible errors sources so that the each spacecraft could be correctly targeted using five trajectory corrections along the way. This paper describes the orbit determination analysis which allowed MER-A to be targeted using only four trajectory correction maneuvers to an entry flight path angle of -11.49 deg +/-O.010 deg (3(sigma)) and MER-B to be targeted using only three trajectory correction maneuvers to an entry flight path angle of -11.47 +/-0.021 deg(3(sigma)).

Mars Exploration Rovers entry, descent, and landing navigation.

During the final approach and Entry, Descent and Landing (EDL) of both Mars Exploration Rovers (MER), one-way Doppler were monitored to detect, in real-time, the following events.

Orbit Determination for the 2007 Mars Phoenix Lander

The Phoenix mission is designed to study the arctic region of Mars. To achieve this goal, the spacecraft must be delivered to a narrow corridor at the top of the Martian atmosphere, which is approximately 20 km wide. This paper will discuss the details of the Phoenix orbit determination process and the effort to reduce errors below the level necessary to achieve successful atmospheric entry at Mars. Emphasis will be placed on properly modeling forces that perturb the spacecraft trajectory and the errors and uncertainties associated with those forces. Orbit determination covariance analysis strongly influenced mission operations scenarios, which were chosen to minimize errors and associated uncertainties.

The NASA Phoenix 2007 Mars Lander thruster calibration estimator: design and validation

The NASA Phoenix 2007 Mars Lander mission, launched in August 2007 on its mission to land near the north pole of Mars in May 2008, had a driving need for entry-corridor delivery precision, which parlayed into stringent requirements on deep space navigation accuracy. This, in turn, necessitated in-cruise calibration of the three-axis thrust force vectors produced by each of the vehicles four reactioncontrol system (RCS) thrusters during frequent daily low-catalyst-bed-temperature firings done to maintain the 3-axis attitude deadbands. A novel recursive sigmapoint consider-covariance filter was designed, validated and ultimately utilized extensively during flight operations, to estimate the RCS force vectors, per individual thruster. The estimate was achieved through ground-based processing of Deep Space Network (DSN) and telemetered gyroscope data from the spacecrafts inertial measurement unit (IMU), using a novel sigma-point consider filter (SPCF) formulation. During early-cruise active calibration, the spacecraft was flown in attitudes chosen, using this filter, to maximize observability of all thruster axes, to an extent constrained by vehicle thermal and communication considerations. The design of the Phoenix thruster calibration filter, and its validation through processing of archived Mars Odyssey thruster calibration radiometric data, and simulated sets of data, are discussed in this paper. The paper concludes with the formulation of the thruster calibration campaign and a summary of the thruster calibration campaign results. The SPCF algorithm is summarized in the Appendix.