A multiple model estimation scheme is proposed to enhance the robustness of a resident space object (RSO) tracker subject to its maneuverability uncertainties (unplanned or unknown jet firing activities) and other system variations. The concept is based on the Interacting Multiple Model (IMM) estimation scheme. Within the IMM framework, two Extended Kalman Filter (EKF) models: (i) a 6 State (Position and Velocity of a constant orbiting RSO) EKF and (ii) a 9 state (Position, Velocity, and Acceleration of a maneuvering RSO) EKF are designed and implemented to achieve RSO maneuvering detection and enhanced tracking accuracy. The IMM estimation scheme is capable of providing enhanced state vector estimation accuracy and consistent prediction of the RSO maneuvering status, thus offering an attractive design feature for future Space Situational Awareness (SSA) missions. The design concept is illustrated using the Matlab/Based Simulation testing environment.
Multi-target tracking is intrinsically an NP-hard problem and the complexity of multi-target tracking solutions usually do not scale gracefully with problem size. Multi-target tracking for on-line applications involving a large number of targets is extremely challenging. This article demonstrates the capability of the random finite set approach to provide large scale multi-target tracking algorithms. In particular it is shown that an approximate filter known as the labeled multi-Bernoulli filter can simultaneously track one thousand five hundred targets in clutter on a standard laptop computer.
A standardized interface has been developed for the integration and accommodation of secondary payloads on to Orbital
Sciences Corporation's StarBus line of GEO-based commercial communications satellites. This standardized interface
through hardware adaptations and methodology incorporates all the major subsystems of the spacecraft and will allow
for a variety of hosted secondary payloads to be accommodated while not interfering with the "spacecraft product line"
manufacturing scheme common on commercial communications satellites. Indeed the low cost and fast schedules,
typically two years from contract start to launch, for commercial communications satellites relies upon a high level of
design standardization and exacting heritage. The Hosted Payloads interface as developed and exercised on the StarBus
makes the hosted payload components look like the usual communications components that are routinely comprise the
standard bent-pipe type of communications payload architecture - the kind of payload that the host spacecraft is
optimized to carry. Furthermore the hosted payload accommodation methodology has been developed to flow into the
timeline of the host spacecraft while still allowing for a small degree of margin. Being able to reconcile the aggressive
development process of a commercial communications satellites with the more elongated process seen in a remote
sensing payload is one necessary step to secure a viable future of commercially hosted payloads.
This paper presents a design concept that can be used to monitor Micro-Electro-Mechanical Systems (MEMS) inertial sensors' random noise characteristics and dynamically track them for cancellation. The concept consists of a two-prong compensation approach offering both filtering and cancellation capability to effectively null out the MEMS sensor noise sources. The first path compensation will be fundamentally designed using high order filtering and calibration concept. This path is intended to effectively calibrate and remove high noise drift errors inherently existing in the MEMS sensors by using external aiding sensors data available on-board the spacecraft such as star tracker or GPS sensors. MEMS sensors' bias, scale factor, and misalignment stability errors will all be taken care of using this first prong design approach. The second compensation system will be designed using signal isolation and stochastic model propagation concept allowing on-line MEMS sensor's noise estimation and characterization. This second path is intended to dynamically monitor changes and identify MEMS inertial sensors' random noise parameters such as scale factor error, angular random walk, angular white noise, and rate random walk in a real-time fashion so that proper noise spectrum signatures can be obtained to update the process noise matrix of the calibration filter. This latter design approach can also be applied and implemented as a signal-conditioning device for MEMS sensors' internal self-calibration. The proposed algorithm is provided along with its preliminary results evaluated using simulation.
It is well known to the Kalman filter design and estimation community that the values for the process noise, Q, and measurement noise, R, covariance matrices primarily dictate the filter performance. In addition, selecting proper values for Q and R is traditionally done in an ad-hoc manner. This paper provides a new look into the roles of the process noise and measurement noise matrices using the spacecraft attitude estimation problem as the design benchmark. This includes an interesting situation where the theoretical values of Q and R, derived as a function of gyro and star tracker noise parameters, are exactly matched with the noise characteristics employed on the sensor model side. However, the filter still exhibits poor attitude estimation performance, as measured against an attitude knowledge requirement, while subject to a high rate slew profile. A simulation based tuning methodology is developed to optimize the filter performance and bring the attitude estimation back to within the required attitude knowledge bound.
Conference Committee Involvement (1)
Modeling, Simulation, and Calibration of Space-based Systems