System for Near-Term Mitigation of Space Debris

Abstract

An improved debris generator that can be used as part of a space object modeling (SOM) system to turn any active spacecraft into a debris sensor for characterizing and cataloging space debris. The space debris generator includes an extractor which extracts satellite sensor data into a sensor store, an impact agent which estimates “on the fly” new debris field objects generated by the collision, and an injector which stores the enhanced object data on a central server for near-term mitigation of impact driven breakup events. The impact agent is an artificial neural network trained using a large number of simulations of impact events.

Claims

1. A method performed by an on-board or ground based computing system for quickly characterizing and cataloging debris strikes using spacecraft sensor data.

2. The method of claim 1 further comprising an extractor which stores satellite sensor information in a sensor store and tests for impact events.

3. The method of claim 1 further comprising a trained impact agent which takes the sensor store information and characterizes the pre and post impact parameters by using sensor information as a target for data driven analysis techniques.

4. The method of claim 3 further comprising a spacecraft impact emulator which provides a virtual environment for training an agent.

5. The method of claim 1 further comprising an injector which injects the computed debris field into a near-term object store for use by other components of a SOM model and near-term debris risk mitigation.

6. The method of claim 1 further comprising multiple on-board or ground based computer systems for calculating debris strike characteristics and sharing data with other nodes in the network for characterizing and cataloging debris.

7. A memory storing computer executable instructions for controlling a computer system to characterize and catalog space debris using in-situ spacecraft data comprising: i) extractor instructions that extract, process, and store satellite sensor information. ii) impact agent instructions that calculate impact dynamics and debris characteristics, including pre and post impact parameters, from the sensor store data. iii) spacecraft impact emulator instructions that can be used to train an agent off-line. iv) injector instructions that store the debris characteristics for use by other SOM components.

Description

DESCRIPTION OF THE DRAWINGS

[0021] For a more complete understanding of the invention, reference is made to the following description and accompanying drawings, in which:

[0022] FIG. 1 is a block diagram that illustrates the extractor, impact agent, and injector of the debris generator system in some embodiments;

[0023] FIG. 2 is a block diagram that illustrates the extractor component of the debris generator system in some embodiments;

[0024] FIG. 3 is a block diagram that illustrates the process for training the impact agent using a spacecraft impact emulator in some embodiments;

[0025] FIG. 4 is a block diagram that illustrates the spacecraft impact emulator in some embodiments; and

[0026] FIG. 5 is a block diagram that illustrates the injector component of the debris generator system in some embodiments.

DETAILED DESCRIPTION OF THE INVENTION

[0027] The following is a detailed description of illustrative embodiments of the present invention. As these embodiments of the present invention are described with reference to the aforementioned drawings, various modifications or adaptations of the methods and or specific structures described may become apparent to those skilled in the art. All such modifications, adaptations, or variations that rely upon the teachings of the present invention, and through which these teachings have advanced the art, are considered to be within the spirit and scope of the present invention. For example, it is apparent that additional analysis techniques operating on other raw spacecraft sensor data or derived measurements, such as from ground based platforms, may also be incorporated as input to the impact agent. Hence, these descriptions and drawings are not to be considered in a limiting sense as it is understood that the present invention is in no way limited to the embodiments illustrated.

[0028] In broad terms, the debris generator may be described as a set of computer executable instructions executed by one or more computers via routines, programs, or data structures that perform particular tasks which can be distributed as desired in various embodiments. The computer system on which the debris generator may be implemented may be an on-board computer system located on the spacecraft, a ground based computer system, computer readable storage media, or other input devices such as a high performance payload data processor. Data may be transmitted to or from the debris generator via wired or wireless (electromagnetic based) connections.

[0029] The present invention provides a method and system for capturing impact information and quickly using it to generate a predicted debris field in order to assess the risk to spacecraft from space debris. Most existing methods for predicting debris fields rely on a disparate patchwork of data and mathematical techniques founded on assumptions which severely limit the accuracy of the results. For example, some methods may use a ballistic limit equation to determine whether a target will be perforated by the impacting object, but completely neglect the system effects of impact on important parameters such as spacecraft velocity or altitude. Other methods may incorporate a more physically detailed computational model of the impact event, but then be forced to rely on inaccurate data about the impacting particle because current impact sampling methods are either too expensive, too slow, or insufficient. The debris generator of the current invention, which in one embodiment is a new active sampling method itself, uses a physically detailed model of the impact event in conjunction with data driven techniques applied to readily available satellite sensor measurements. This requires the invention of a new component, the impact agent (block 102), and a new framework for handling the information, the extractor and injector (blocks 101 and 103). FIG. 1 is a flow diagram showing how these components fit together.

[0030] FIG. 2 is a flow diagram for the extractor, whose main function is to store satellite sensor data in a sensor store (101.1) that can be polled for new data (block 101.2) and accessed by the impact agent when a perturbation to the satellite's orbital motion has occurred (block 101.3). The sensor store can include data transmitted directly from available satellite sensors or derived data based on measurements. For example a spacecraft's spin axis can be computed either from on board sensors, or satellite ground tracking information such as antenna modulation or photometry observation. Other embodiments of data that can be housed in the sensor store are delta satellite mean altitude (dSMA) and spacecraft state vectors which include attitude, rate, and spacecraft reaction wheel information. Data may be transmitted to/from the sensor store locally on the spacecraft and/or to/from other nodes in the broader network via wired or wireless (electromagnetic based) connections.

[0031] Block 102 of FIG. 1 is the impact agent. The primary output from the impact agent are the velocity vectors of predicted debris. The impact agent is an unsupervised, re-inforced learning computer algorithm which is trained in the spacecraft impact emulator environment to recognize policies corresponding to measured sensor data. In some embodiments that measured sensor data can be the spacecraft change in momentum, p.sub.post (Equation 1), computed with knowledge of the spacecraft's change in orbital radius using the instantaneous velocity:

[00002]$\begin{array}{cc}{v}^2=\frac{1}{2}{v}_e^2\left(\frac{2}{r}-\frac{1}{a}\right)& \left(2\right)\end{array}$

where:
v=spacecraft orbital velocity
v.sub.e=escape velocity
r=spacecraft orbital radius
a=spacecraft semi-major axis

[0032] In other embodiments, the sensor data used as a target for the trained impact agent can be some combination of parameters including spacecraft state vectors, spin axis, and rotation rates.

[0033] The impact agent shown in FIG. 1 contains learned policies correspond to the coupled non-linear physical processes—spacecraft kinematics, shock physics, and orbital mechanics—that map the input parameter state generated by the extractor to the output state stored by the injector. In some embodiments, the impact agent may be trained using a policy gradient algorithm to optimize the post-impact target by following the gradient towards higher rewards. In another embodiment, the impact agent may apply a policy search algorithm to search the pre-impact parameter space for the combination which achieves the desired post impact target.

[0034] FIG. 3 is a flow diagram showing how the impact agent is trained to learn a given goal (block 102.1), such as post impact momentum p.sub.post, using available actions (block 102.3). The actions are the set of available input parameters that can be explored by the training algorithm (block 102.2). In some embodiments, the actions can include:

[00003]${\stackrel{.fwdarw.}{h}}_i=\left[\begin{array}{c}{m}_{\mathrm{obj},i}\\ v_{\mathrm{obj},i}\\ {\rho }_{\mathrm{obj},i}\\ l_{\mathrm{obj},i}\\ Z_{\mathrm{obj},i}\\ R_s\\ \theta \end{array}\right]$

where:
m.sub.obj,i=mass of impacting object
v.sub.obj,i=velocity of impacting object
ρ.sub.obj,i=density of impacting object
l.sub.obj,i=characteristic length of impacting object
Z.sub.obj,i=shock impedance of impacting object
R.sub.s=impact location
θ=impact angle

[0035] For a given set of actions, the spacecraft impact emulator (block 102.4) generates the corresponding post impact results. In some embodiments, the results can be:

[00004]$\stackrel{.fwdarw.}{r_i}=\left[\begin{array}{c}{\beta }_{\mathrm{obj},i}\\ p_{post}\\ n_{deb}\\ {\stackrel{.fwdarw.}{n}}_{deb,i}\end{array}\right]$

where:
β.sub.obj,i=momentum enhancement factor
p.sub.post=post impact momentum of the spacecraft
n.sub.deb=number of debris cloud fragments generated
{right arrow over (n)}.sub.deb,i=velocity vector of debris fragment

[0036] Using these results, which in the training process are referred to as observations (block 102.5), the impact agent receives rewards (block 102.6) which reinforce pathways that lead to the target post impact parameter space. In some embodiments, the impact agent may use a discount factor to evaluate an action based on the sum of all rewards that follow. To those practiced in the art, this is known as the credit assignment problem.

[0037] FIG. 4 is a flow diagram showing how the spacecraft impact emulator is constructed. As shown in FIG. 4, N hydrocode simulations (block 103.4), unique to a given spacecraft, are completed in order to generate training data for a data driven technique (blocks 103.1-103.3). In some embodiments, the data driven technique may be a Multi-Layer Perceptron (MLP) using seven (7) input layers {right arrow over (h)}.sub.i (block 103.1), multiple hidden layers, and four (4) output layers {right arrow over (r)}.sub.i (block 103.3). In other embodiments the input layer may contain another number of input parameters, d, and output parameters, o, such that the spacecraft impact emulator is trained to learn a function ƒ(⋅): R.sup.d.fwdarw.R.sup.o.

[0038] FIG. 5 is a flow diagram showing how the injector is constructed. Results from the trained impact agent are ingested into an object store (block 104.1). The object store, which may be a database or a flatfile, includes an entry for each spacecraft that contains its orbital parameters and results from the trained impact agent calculations. The object store may be initialized based on the two-line element (TLE) of orbiting elements as provided by NORAD. Data from the agent may be transmitted to an object store locally on the spacecraft and/or transmitted to/from other nodes in the broader network via wired or wireless (electromagnetic based) connections.