Proc. SPIE. 11017, Sensors and Systems for Space Applications XII
KEYWORDS: Information fusion, Data modeling, Sensors, Satellites, Control systems, Satellite navigation systems, Meteorological satellites, Satellite communications, Environmental sensing, Systems modeling
Advancements in artificial intelligence, information communication, and systems design are potential for autonomous systems emerging for space situation awareness (SSA) architectures. Examples of architecture designs are autonomy in motion (AIM) for dynamic data assessment systems (e.g., robotics) and autonomy at rest (AAR) for static data collection systems (e.g., surveillance). However, there is a need for data architectures which are tailored to the SSA missions, which necessitates autonomy in use (AIU). AIU requires pragmatic use of message passing and data flow architectures, contextual and theoretic modeling, and user and information fusion. Information fusion provides methods for data aggregation, correlation, and temporal assessment and awareness. Together, AIU accesses the dynamic data for autonomy in change (AIC), information fusion from AAR in order to make AIM real-time decisions. The paper discusses issues for space situation awareness directions focusing on autonomy in use.
We propose the diffusion-based enhanced covariance intersection cooperative space object tracking (DeCiSpOT) filter. The main advantage of the proposed DeCiSpOT algorithm is that it can balance the computational complexity and communication requirements between different sensors as well as improve track accuracy when measurements do not exist or are of low accuracy. Instead of using the standard covariance intersection in the diffusion step, the enhanced diffusion strategy integrates the 0-1 weighting covariance intersection strategy and the iterative covariance intersection strategy. The proposed DeCiSpOT algorithm also uses the global nearest neighbor and probabilistic data association for multiple space object tracking. Two typical scenarios including cooperative single and multiple space object tracking are used to demonstrate the performance of the proposed DeCiSpOT filter. Using simulated ground-based electro-optical (EO) measurements for multiple resident space objects and multiple distributed EO sensors, the DeCiSpOT archived results comparable to an optimal centralized approach. The results demonstrate that the DeCiSpOT is effective for space object tracking problem with results close to the optimal centralized cubature Kalman filter.
This paper revises and evaluates an orbital emulator (OE) for space situational awareness (SSA). The OE can produce 3D satellite movements using capabilities generated from omni-wheeled robot and robotic arm motions. The 3D motion of satellite is partitioned into the movements in the equatorial plane and the up-down motions in the vertical plane. The 3D actions are emulated by omni-wheeled robot models while the up-down motions are performed by a stepped-motorcontrolled- ball along a rod (robotic arm), which is attached to the robot. Lidar only measurements are used to estimate the pose information of the multiple robots. SLAM (simultaneous localization and mapping) is running on one robot to generate the map and compute the pose for the robot. Based on the SLAM map maintained by the robot, the other robots run the adaptive Monte Carlo localization (AMCL) method to estimate their poses. The controller is designed to guide the robot to follow a given orbit. The controllability is analyzed by using a feedback linearization method. Experiments are conducted to show the convergence of AMCL and the orbit tracking performance.
This paper presents an anti-jamming Global Positioning System (GPS) receiver antenna testing system. The system is composed of a set of six circular rails with different radii that are installed to emulate GPS satellite orbits, a set of GPS antennas each carried by a trolley that can move on the rails to emulate GPS satellites, a trolley movement controller to emulate the GPS satellite constellation propagation, and a multi-channel GPS simulation system that provides GPS signal and GPS satellite state position information. The GPS receiver antenna under test is at the center of the rails. As the GPS antennas carried by trolleys move on the rail to emulate the GPS satellite constellation propagation, the GPS receiver antenna under test receives the emulated GPS signals. The GPS signals’ arrival direction is almost the same as that coming from real GPS satellites. The anti-jamming GPS receiver antenna testing system can emulate a GPS satellite constellation with multiple GPS satellites; with high emulation accuracy (in both GPS signal phase and satellite angular position with respect to the GPS receiver antenna under test); requiring only a single phase calibration at the beginning of each test; and can support a 4 hours test / emulation.
In this paper, the dynamic enhanced cubature Kalman filter is proposed to explore the constraint of the long-term relationship of system states. The performance of the proposed dynamic enhanced cubature Kalman filter (DECKF) is compared to the conventional cubature Kalman filter via two numerical examples. The simulation results show that the proposed filter can provide better performance than conventional cubature Kalman filter, for certain scenarios.
In a cognitive reasoning system, the four-stage Observe-Orient-Decision-Act (OODA) reasoning loop is of interest. The OODA loop is essential for the situational awareness especially in heterogeneous data fusion. Cognitive reasoning for making decisions can take advantage of different formats of information such as symbolic observations, various real-world sensor readings, or the relationship between intelligent modalities. Markov Logic Network (MLN) provides mathematically sound technique in presenting and fusing data at multiple levels of abstraction, and across multiple intelligent sensors to conduct complex decision-making tasks. In this paper, a scenario about vehicle interaction is investigated, in which uncertainty is taken into consideration as no systematic approaches can perfectly characterize the complex event scenario. MLNs are applied to the terrestrial domain where the dynamic features and relationships among vehicles are captured through multiple sensors and information sources regarding the data uncertainty.
This paper develops and evaluates an orbital emulator (OE) for space situational awareness (SSA). The OE can produce 3D satellite movements using capabilities generated from omni-wheeled robot and robotic arm motion methods. The 3D motion of a satellite is partitioned into the movements in the equatorial plane and the up-down motions in the vertical plane. The 3D actions are emulated by omni-wheeled robot models while the up-down motions are performed by a stepped-motor-controlled-ball along a rod (robotic arm), which is attached to the robot. For multiple satellites, a fast map-merging algorithm is integrated into the robot operating system (ROS) and simultaneous localization and mapping (SLAM) routines to locate the multiple robots in the scene. The OE is used to demonstrate a pursuit-evasion (PE) game theoretic sensor management algorithm, which models conflicts between a space-based-visible (SBV) satellite (as pursuer) and a geosynchronous (GEO) satellite (as evader). The cost function of the PE game is based on the informational entropy of the SBV-tracking-GEO scenario. GEO can maneuver using a continuous and low thruster. The hard-in-loop space emulator visually illustrates the SSA problem solution based PE game.
For the short-arc angle only orbit initialization problem, the admissible area is often used. However, the accuracy using a single sensor is often limited. For high value space objects, it is desired to achieve more accurate results. Fortunately, multiple sensors, which are dedicated to space situational awareness, are available. The work in this paper uses multiple sensors’ information to cooperatively initialize the orbit based on the fusion of multiple admissible areas. Both the centralized fusion and decentralized fusion are discussed. Simulation results verify the expectation that the orbit initialization accuracy is improved by using information from multiple sensors.
This paper presents a low size, weight and power – cost (SWaP-C) airborne sense and avoid (ABSAA) system, which is based on a linear frequency modulated continuous wave (LFMCW) radar and can be mounted on small unmanned aircraft system (UAS). The system satisfies the constraint of the available sources on group 2/3 UAS. To obtain the desired sense and avoid range, a narrow band frequency (or range) scanning technique is applied for reducing the receiver’s noise floor to improve its sensitivity, and a digital signal integration with fast Fourier transform (FFT) is applied to enhance the signal to noise ratio (SNR). The gate length and chirp rate are intelligently adapted to not only accommodate different object distances, speeds and approaching angle conditions, but also optimize the detection speed, resolution and coverage range. To minimize the radar blind zone, a higher chirp rate and a narrowband intermediate frequency (IF) filter are applied at the near region with a single antenna signal for target detection. The offset IF frequency between transmitter (TX) and receiver (RX) is designed to mitigate the TX leakage to the receiver, especially at close distances. Adaptive antenna gain and beam-width are utilized for searching at far distance and fast 360 degree middle range. For speeding up the system update rate, lower chirp rates and wider IF and baseband filters are applied for obtaining larger range scanning step length out of the near region. To make the system working with a low power transmitter (TX), multiple-antenna beamforming, digital signal integration with FFT, and a much narrower receiver (RX) bandwidth are applied at the far region. The ABSAA system working range is 2 miles with a 1W transmitter and single antenna signal detection, and it is 5 miles when a 5W transmitter and 4-antenna beamforming (BF) are applied.
In this paper, a hybrid Monte Carlo Gauss mixture Kalman filter is proposed for the continuous orbit estimation problem. Specifically, the graphic processing unit (GPU) aided Monte Carlo method is used to propagate the uncertainty of the estimation when the observation is not available and the Gauss mixture Kalman filter is used to update the estimation when the observation sequences are available. A typical space object tracking problem using the ground radar is used to test the performance of the proposed algorithm. The performance of the proposed algorithm is compared with the popular cubature Kalman filter (CKF). The simulation results show that the ordinary CKF diverges in 5 observation periods. In contrast, the proposed hybrid Monte Carlo Gauss mixture Kalman filter achieves satisfactory performance in all observation periods. In addition, by using the GPU, the computational time is over 100 times less than that using the conventional central processing unit (CPU).
This paper develops and evaluates a satellite orbital testbed (SOT) for satellite communications (SATCOM). SOT can emulate the 3D satellite orbit using the omni-wheeled robots and a robotic arm. The 3D motion of satellite is partitioned into the movements in the equatorial plane and the up-down motions in the vertical plane. The former actions are emulated by omni-wheeled robots while the up-down motions are performed by a stepped-motor-controlled-ball along a rod (robotic arm), which is attached to the robot. The emulated satellite positions will go to the measure model, whose results will be used to perform multiple space object tracking. Then the tracking results will go to the maneuver detection and collision alert. The satellite maneuver commands will be translated to robots commands and robotic arm commands. In SATCOM, the effects of jamming depend on the range and angles of the positions of satellite transponder relative to the jamming satellite. We extend the SOT to include USRP transceivers. In the extended SOT, the relative ranges and angles are implemented using omni-wheeled robots and robotic arms.
This paper develops and evaluates a pursuit-evasion (PE) game approach for elusive orbital maneuver and space object tracking. Unlike the PE games in the literature, where the assumption is that either both players have perfect knowledge of the opponents’ positions or use primitive sensing models, the proposed PE approach solves the realistic space situation awareness (SSA) problem with imperfect information, where the evaders will exploit the pursuers’ sensing and tracking models to confuse their opponents by maneuvering their orbits to increase the uncertainties, which the pursuers perform orbital maneuvers to minimize. In the game setup, each game player P (pursuer) and E (evader) has its own motion equations with a small continuous low-thrust. The magnitude of the low thrust is fixed and the direction can be controlled by the associated game player. The entropic uncertainty is used to generate the cost functions of game players. The Nash or mixed Nash equilibrium is composed of the directional controls of low-thrusts. Numerical simulations are emulated to demonstrate the performance. Simplified perturbations models (SGP4/SDP4) are exploited to calculate the ground truth of the satellite states (position and speed).
This paper presents a time division multiple access (TDMA) multiple-input and multiple-output (MIMO) synthetic aperture radar (SAR) with a sliding range window for automated position-keeping, which can be applied in vessel tracking/escorting, offshore deepwater drillship equipment servicing, etc. A MIMO SAR sensor predefines a special part of the target (i.e., the drillship, ship, or submarine) as the measurement target and does not need special assistant devices/targets installed on the target vessel/platform, so its application is convenient. In the measurement process, the sensor scans the target with multiple ranging gates, forms images of multiple sections of the target, detects the predefined part/target in these images, and then obtains the range and angle of the predefined target for relative localization. Our MIMO SAR has 13 transmitting antennas and 8 receiving antennas. All transmitting antennas share a transmitter and all receiving antennas share a receiver using switches to reduce cost. The MIMO SAR radar has 44 effective SAR phase centers, and the azimuth angle resolution is θ0.5/44 (finest, θ 0.5 is the antenna element’s 3dB beamwidth). The transmitter transmits a chirped linear frequency modulated continuous wave (LFMCW) signal, and the receiver only processes the signal limited in the beat frequency region defined by the distance from the measurement target to the sensor and the interested measurement target extension, which is determined by the receiver bandwidth. With the sliding range window, the sensor covers a large range, and in the covered range window, it provides high accuracy measurements.
Space situational awareness (SSA) is critical to many space missions serving weather analysis, communications, and navigation. However, the number of sensors used in space situational awareness is limited which hinders collision avoidance prediction, debris assessment, and efficient routing. Hence, it is critical to use such sensor resources efficiently. In addition, it is desired to develop the SSA sensor management algorithm in a distributed manner. In this paper, a distributed sensor management approach using the negotiation game (NG-DSM) is proposed for the SSA. Specifically, the proposed negotiation game is played by each sensor and its neighboring sensors. The bargaining strategies are developed for each sensor based on negotiating for accurately tracking desired targets (e.g., satellite, debris, etc.) . The proposed NG-DSM method is tested in a scenario which includes eight space objects and three different sensor modalities which include a space based optical sensor, a ground radar, or a ground Electro-Optic sensor. The geometric relation between the sensor, the Sun, and the space object is also considered. The simulation results demonstrate the effectiveness of the proposed NG-DSM sensor management methods, which facilitates an application of multiple-sensor multiple-target tracking for space situational awareness.
Recently bio-inspired rendezvous strategies have been investigated for applications in space situation awareness.
Particularly, closed-loop solutions have been developed for the cases that the target object is in a circular orbit without
considering any orbital perturbations. In this paper, the minimum-fuel consumption bio-inspired motions are further
studied. The follow cases considering the J2 perturbation, the non-zero eccentricities, and different boundary conditions
are analyzed: (1) the target object is at the local vertical local horizontal coordinate origin; (2) the target is moving in the
local vertical local horizontal coordinate; (3) the rendezvous object approaches the target object from the R-bar, V-bar,
and Z-bar directions, respectively. Fast solutions can be obtained for the rendezvous object to approach the target object
with minimum energy consumption.
In this paper, quantum technology is introduced with three key topics, including quantum computing, quantum communication, and quantum devices. Using these dimensions of quantum techniques we briefly introduce their contributions to aerospace applications. The paper will help readers to understand the basic concepts of the quantum technology and their potential applications in space, air, and ground applications such as highly accurate target positioning.