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23 December 2022

Deep Learning Methods for Space Situational Awareness in Mega-Constellations Satellite-Based Internet of Things Networks

,
and
1
Signal Processing for Telecommunications and Economics Laboratory, Roma Tre University, 00145 Rome, Italy
2
Leonardo Labs, Leonardo S.p.a., 00131 Rome, Italy
*
Author to whom correspondence should be addressed.
Sensors2023, 23(1), 124;https://doi.org/10.3390/s23010124
This article belongs to the Special Issue Satellite Based IoT Networks for Emerging Applications
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Abstract

Artificial Intelligence of things (AIoT) is the combination of Artificial Intelligence (AI) technologies and the Internet of Things (IoT) infrastructure. AI deals with the devices’ learning process to acquire knowledge from data and experience, while IoT concerns devices interacting with each other using the Internet. AIoT has been proven to be a very effective paradigm for several existing applications as well as for new areas, especially in the field of satellite communication systems with mega-constellations. When AIoT meets space communications efficiently, we have interesting uses of AI for Satellite IoT (SIoT). In fact, the number of space debris is continuously increasing as well as the risk of space collisions, and this poses a significant threat to the sustainability and safety of space operations that must be carefully and efficiently addressed to avoid critical damage to the SIoT networks. This paper aims to provide a systematic survey of the state of the art, challenges, and perspectives on the use of deep learning methods for space situational awareness (SSA) object detection and classification. The contributions of this paper can be summarized as follows: (i) we outline using AI algorithms, and in particular, deep learning (DL) methods, the possibility of identifying the nature/type of spatial objects by processing signals from radars; (ii) we present a comprehensive taxonomy of DL-based methods applied to SSA object detection and classification, as well as their characteristics, and implementation issues.

1. Introduction

Artificial Intelligence (AI) is now a part of the daily life of the majority of people. AI is based on the idea that computers can (be programmed to) think just like humans, creating analyses, reasoning, understanding, and getting answers for and from different situations. The big step within the studies of AI is the development of systems capable of learning and developing on their own, or of creating new deductions from the junction of various fragmented information, just as happens within the neurological system of human beings. Therefore, AI is the technology that provides intelligent machines to solve problems, increase productivity, and improve areas such as health, finance, marketing, sales, customer service, and agriculture, among many other fields of application [1,2,3].
The Internet of Things (IoT), on the other hand, is a world with smart technology that impacts almost every aspect of our society. When we think about IoT, we think about technological tools used in everyday life, such as connected thermostats, home security systems, and cars [4,5,6]. The Artificial Intelligence of Things (AIoT) is the sweet spot that combines the best of both worlds: it leverages the technical capabilities of the IoT and makes the best use of the data processing and interpretation capabilities of AI to deliver advanced solutions to users, meeting their specific needs and use cases [7,8]. One of the main benefits of AI in action in an IoT environment is real-time decision-making. The results are obtained instantaneously. For example, an AI-based surveillance system with object detection technology can instantly detect work or environmental hazards, such as sparks from equipment or an employee’s fall, and immediately report notifications. Advanced systems can even make decisions and trigger incident responses on their own [9,10,11].
The IoT technology is as promising as it can be, but it is not without its fair share of implementation shortcomings and challenges. Simplifying data from multiple sources is challenging; one needs to work on infrastructure requirements to examine data security and privacy, etc. The recent scientific and technological advances led by AIoT have also allowed for many opportunities in the space industry, which is a type of field that has been increasingly developed over the last decade, and very significant benefits are expected soon in the field of Satellite for IoT (SIoT). In the harsh industrial environment, the necessity for joining IoT and satellite communications is needed [12]. Many studies have been carried out, especially for low-orbit satellites, due to their interesting characteristics, such as low latency and large capacity [13,14,15,16,17]. As a consequence, IoT applications through satellite systems are developing quickly and promise to be the biggest prospect for the satellite market of the future.
IoT applications will be very well supported by satellite mega-constellations. These are systems with thousands, and even tens of thousands, of satellites in low Earth orbit (LEO), and they are nowadays becoming a reality [18,19,20]. Among all of Earth’s orbital regimes, LEO, generally defined as the region in space between ~160 to 2000 km in altitude [21], is by far the most congested (Figure 1). As the closest to Earth and, therefore, the cheapest to reach, LEO is the most popular orbital regime for satellite deployments [22]. Companies are placing satellites into orbit at an unprecedented frequency to build mega-constellations of communications satellites in LEO. In two years, the number of active and defunct satellites in LEO has increased by over 50% to about 5000. SpaceX alone is on track to add 11,000 more as it builds its Starlink mega-constellation and has already filed for permission with the Federal Communications Commission (FCC) for another 30,000 satellites [23]. Others have similar plans, including OneWeb, Amazon, Telesat, and GW, a Chinese state-owned company. Thousands of satellites and 1500 rocket bodies provide considerable mass in LEO, which can break into debris upon collisions, explosions, or degradation in the harsh space environment. Fragmentations increase the cross-section of orbiting material and with it, the collision probability per time. Eventually, collisions could dominate on-orbit evolution, a situation called the Kessler Syndrome [24].
Figure 1. Different types of satellite orbits around the Earth.
There is much debris or “space junk” in the near-Earth space environment that is large enough to threaten human spaceflights and robotic missions but too small to be tracked. The International Space Station (ISS) and other spacecraft with humans on board, due to the exponential growth of space debris, could be endangered. This debris population includes both natural meteoroids and man-made orbital debris. This debris population includes both natural meteoroids orbiting the sun, and man-made orbital debris, orbiting the Earth, called “orbitals”. Orbital debris generally is any man-made object in Earth’s orbit that no longer serves a useful function, such as non-functioning spacecraft, abandoned launch vehicle stages, mission-related debris, and fragmentation debris. They are constantly tracked, and more than 27,000 pieces have been detected so far. Since both debris and spacecraft travel at extremely high speeds (about 25,267 km/h), the impact of collision of even a tiny piece of orbital debris with a spacecraft could create serious and dramatic problems. There are half a million pieces of debris the size of a marble or larger (up to 0.01016 m, or 1 cm), and about 100 million pieces of debris about 0.001016 m (or one millimeter) and larger. There is also even smaller micrometer-sized debris (9.906 × 10−7 m in diameter) and about 23,000 pieces of debris larger than a softball orbiting the Earth. Even small flecks of paint can damage a spacecraft when traveling at these speeds. For example, many space shuttle windows have been replaced due to some damage caused by paint stains. Simulations of the long-term evolution of debris suggest that LEO is already in the protracted initial stages of the Kessler Syndrome but that this could be managed through active debris removal [25]. The addition of satellite mega-constellations and the general proliferation of low-cost satellites in LEO stresses the environment further [26,27,28]. Hence, decades of the world’s space activities have left LEO cluttered with active satellites and littered with orbital debris. Recent reports [29,30] estimate that there are about 28,210 debris objects regularly tracked by Space Surveillance Networks. While large commercial satellite constellations such as SpaceX’s Starlink undeniably offer tremendous potential for the satellite industry, they inevitably increase the probability of mutual collisions among orbiting objects due to the inherently high number of satellites involved in large constellations. This poses a significant threat to the sustainability and safety of space operations that must be carefully and efficiently addressed.
The operation of monitoring the space environment and resident space objects, knowing and characterizing space objects and their operational environment, is known as space situational awareness (SSA) [31]. SSA is a fundamental part of Space Domain Awareness (SDA), which represents the capacity of understanding the actual and expected working conditions in space. In more detail, SSA focuses on the problems related to (i) space object tracking, (ii) identification, (iii) determining their orbits, (iv) gaining knowledge about the scenario in which they are working, and (v) forecasting their upcoming positions and risks to their functioning (Figure 2). SSA is hence of fundamental importance to all space traffic management (STM) operations, and one of the fundamental aspects of SSA is to calculate and act in response to debris from fragmentation events, meteor storms, or other natural events that will be very dangerous for all the space systems. One critical task is to classify space objects according to their properties. Unfortunately, the information on space objects is often limited. Typically, the visual magnitude and the radar cross section (RCS) of space objects can be obtained via optical and radar sensors, respectively. Artificial Intelligence (AΙ) and machine learning (ML) systems appear to be very promising for detecting and classifying these kinds of objects. Methods for assessing behavioral analysis [32] and autonomy [33,34], reported in the literature, include Artificial Neural Networks (ANN) [35,36], Support Vector Machines (SVM) [37], reinforcement learning [38,39], and deep neural networks [31].
Figure 2. Space Situational Awareness: objectives and enabling technologies.
These ML methods, as well as deep learning (DL) methods [40], support evidence-based knowledge for SDA [41] of space-domain sensor fusion programs such as DARPA Hallmark [42]. SSA also includes the understanding of mission policies, technical aims, and orbital mechanics [43,44,45]. In addition, SSA benefits from game-theory studies in supporting pursuit-evasion analysis [46,47,48,49,50] and from gathering data to track satellites, debris, and natural phenomena [51,52,53,54,55,56,57]. Then, in order to have effective SSA results, tracking efforts must coordinate with detection policies [58,59], waveform selection [60], and attack mitigation [61,62,63,64].
Other recent methods for effective SSA include time-delay Neural networks (TDNN) [65], ML and CNN networks [66,67,68,69,70], clustering [71], orbital control theory [72], a game theoretic approach, namely Adaptive Markov Inference Game Optimization (AMIGO) engine [73,74,75,76], and Deep Reinforcement Learning [77,78,79]. Even more recently, new trends in the literature are focusing on agile, intelligent, and efficient computer vision architectures operating on quantum neuromorphic computing as part of an SSA network [80]. Quantum neuromorphic vision, in conjunction with polarimetric Dynamic Vision Sensors p(DVS) principles, represent the SSA of tomorrow, working at very high speeds with low requirements for bandwidth, power, and memory.
In this paper, we will focus on and review methods belonging to a specific subgroup of SSA, namely on space debris automatic detection, since millimeter-sized orbital debris poses the highest end-of-mission risk for most spacecraft roboticists operating in LEO [81,82]. In this context, this work aims at providing an overview of recent AI and AIoT for space applications by examining the current state of the art and discussing future opportunities to support efforts in space exploration. The rest of our paper is organized as follows. In Abbreviations, a list of the used acronyms is reported. Section 2 is divided into three sub-sections and illustrates SSA space debris applications, with and without the use of deep learning methods. The last part of Section 2 shows some interesting discussions about the use of new types of neural networks. Subsequently, in Section 3, we will discuss the case study and the experimental setup. The results, used as a proof of concept of some of the reviewed DL methods for space debris detection, are then shown and discussed in Section 4, while Section 5 briefly concludes our work, outlining the strong and weak points of these technologies.

3. Case Study

In this and the following section, we show preliminary results and comparisons by applying deep learning methods for SSA applications in a simulated environment. Although deep learning could be employed for several different tasks, as well as in different stages of a generic processing chain, in our tests, we will focus on a well-defined and paradigmatic use case, i.e., small moving object detection in LEO from radar signals. To do that, we simulate the standard processing chain of a monostatic pulse-Doppler radar that detects the radial velocity of moving targets at specific ranges. The radar output is used to feed a neural network architecture that provides the number of detected targets, as shown in Figure 4.
Figure 4. Flowchart of the experimental evaluation presented in this paper.

3.1. Radar Processing

At the receiver, the electromagnetic echoes reflected by a target are split into their I and Q (in-phase and quadrature) components by using coherent demodulation. As a propagation form, we assumed a two-rays ground reflection model, while targets have been characterized in terms of distance (i.e., range) from the radar, velocity, and Radar Cross Section (RCS). Once the I and Q components are extracted, digital sampling is performed after a signal windowing that controls the sidelobes level caused by the sampling operation. Afterward, pulse-Doppler signal processing separates reflected signals into channels by means of a set of filters for each ambiguous range. The maximum unambiguous range is related to the inverse of the Pulse Repetition Frequency (PRF) of the radar. More in-depth, the I and Q signals are filtered by the following scheme: the samples are reshaped into a time domain matrix; columns correspond to range samples (fast time), while rows correspond to pulse intervals (slow time). Convolved with the matched filter by means of a Fast Fourier Transform operation, the output matrix provides a power spectral density estimate of the returned signal in function of the range and Doppler frequency. This matrix is also known as a range-Doppler map. In a classical pulse-Doppler radar processing chain, the estimation of target position and velocity is performed by thresholding the range-Doppler map and finding the range and the Doppler bins in which the energy exceeds the given threshold. In our experiments, these maps are the input of the neural network architectures that we want to compare. For further details on radar signal processing, the reader may refer to [113].

3.2. Neural Network Frameworks

The range-Doppler maps are then used as inputs for the deep learning frameworks that we use for comparisons. We test convolutional neural networks to work correctly and efficiently with 2D image inputs. The chosen networks were SqueezeNet, VGG-16, AlexNet, and GoogLeNet, adjusting their parameters and levels for this specific use case:
  • SqueezeNet is a very light architecture which, nevertheless, achieves outstanding performance in computer vision tasks. The basic idea of the SqueezeNet network is to create a small neural network with few parameters, which can easily adapt to portable devices, thus having a lower computational burden, lower memory demand, and reduced inference time. It is made up of 18 layers. The compression layer consists of 1-by-1 convolutions, which combine all input data channels into a single channel. This procedure reduces the number of inputs for the next level. Data reduction is obtained also by using max-pooling layers, which perform a pooling operation that calculates and retains the maximum value of each patch inside each feature map. In a SqueezeNet, the last learnable layer is the final convolutional layer, unlike in most networks, where the last layer with learnable weights is generally a fully connected layer.
  • VGG-16 is a sixteen-layer deep neural network with about 138 million parameters. This implies that it takes a long time to be trained and to make an inference. It also occupies a significant amount of memory (roughly 533 MB). Despite this, it has been used in several image classification problems, and in terms of performance, it provided the best result with a test error of about 7.0%. [114,115]. The main design idea was to increase the depth by using smaller (3 × 3) multiple convolutional filters than those of previous networks. VGG-16 is composed of a stack of 13 convolutional layers followed by three fully connected layers. Each convolutional block consists of multiple convolutional layers, each with different 3 × 3 filter kernels. As the depth increases, the number of filters in the levels grows, from 64 up to 512, in order to extract increasingly detailed feature maps from each block. The convolutional layers are followed by a rectified linear unit activation layer, and each block ends with a maximum pooling layer, with a 2 × 2 sliding filter with step 2. At the end of the network, there are three fully connected layers: the first two layers have 4096 nodes each; the third performs the 1000-way ILSVRC classification and, therefore, contains 1000 output nodes. This final layer comes with a soft-max activation function for classification. Since VGG-16 is computationally demanding and the output layer does not match our case study, we customized it by modifying the last layer. We set the number of output nodes equal to the number of classes that we have defined in our use case (4, as we will describe later in the next section). As a final step, we froze the first ten layers, and we performed new training to learn new network weights for the last layers.
  • The AlexNet architecture consists of five layers with a combination of max pooling, followed by three fully connected layers [116]. It uses rectified linear units instead of a hyperbolic tangent function. The advantage is twofold: it is faster to compute, especially during network training, and it mitigates the problem of vanishing gradients [117]. An interesting property of this network is that it allows parallel-GPU training by placing half of the model neurons on one GPU and the other half on the second one. This allows us to train larger models or to reduce training time. Moreover, dropout layers are used to avoid overfitting. This technique works by randomly firing a set of neurons within the first two fully connected layers during the whole training. The price to pay is that it increases the training time required for model convergence. AlexNet demonstrated significantly higher performance by achieving high accuracy on very challenging datasets, and it can be credited with bringing deep learning to other fields, such as natural language processing and medical image analysis [118].
  • GoogLeNet, developed by Google, was responsible for creating a new state of the art for classification and detection tasks in the ILSVRC. It also has been used for other computer vision activities, such as face detection and recognition, or in adversarial training. GoogLeNet’s architecture is 22 levels deep, with 27 pool levels included. There are nine starter modules stacked linearly. The ends of the startup modules are connected to the global average pooling level. Since neural networks are time-consuming and expensive to train, the number of input channels is limited. The first level of convolutions uses a filter size of 7 × 7, which is relatively large compared to other kernels within the network. The main purpose of this layer is to immediately reduce the input image without losing spatial information. To address the overfitting problem, the GoogLeNet architecture was created with the idea of having multi-dimensional filters that can operate on the same level. With this idea, the network becomes wider rather than deeper. For ILSVRC 2014, GoogLeNet ranked first with an error rate of 6.67% [119].

4. Results and Discussions

In order to carry out our experiments, we have built a dataset that has been structured as follows: range-Doppler maps have been generated as the position and speed varied, and by assigning to each spectrum one of four possible labels, where each represented the number of targets detected (zero if there are no objects, one if there is only one target and so on). Once the images were generated, they were given as input to neural networks for classification. The networks, therefore, classify whether the image, when the position of the object and the speed at which it moves varies, belongs to the first, second, third, or fourth case mentioned. Figure 5, Figure 6 and Figure 7 show the range-Doppler maps in presence of no object, and one, two, and three objects moving. The positions of each target change from a range from 1 to 3000 m, and the maximum detectable speed is 225 m/s.
Figure 5. Example of range-Doppler maps generated with (respectively, from the upper left image to the lower right image) 0, 1, 2, 3 targets.
Figure 6. Example of range-Doppler maps generated with fixed positions at different speeds of (respectively, from the left to right) 1, 2, 3 targets.
Figure 7. Example of range-Doppler maps generated with speeds of 225 m/s and different positions of (respectively, from the left to right) 1, 2, 3 targets.
The entire dataset consists of 800 images, and it was exploited as follows: 640 images (80% of the dataset) were used for the training phase, while 160 images (20% of the dataset) were used for testing the networks. In addition, we used the stochastic gradient descent with momentum (SGDM) optimizer, and the learning rate is 0.0003 with 50 epochs and 50 batch sizes. Hyperparameters are chosen by looking at the literature experiences and best practices. In this sense, hyperparameter optimization is out of the scope of this study. Nevertheless, it should be explored in the future and extended works.
As an example, Figure 8 illustrates the training curves of the SqueezeNet network (the learning curves of the other approaches are not reported here for the sake of space).
Figure 8. Learning curves of the SqueezeNet DL method.
To understand the performance of the considered deep learning classifiers, we have exploited the following metrics:
Accuracy = TP + TN TP + TN + FP + FN
Precision = TP TP + FP
Recall = TP TP + FN
F − Measure = 2   Precision   Recall Precision + Recall
True positives (TP) are the number of samples correctly classified as belonging to its true class, while true negatives (TN) are the number of samples correctly classified as not belonging to a given class. False positives (FP) are the number of samples misclassified as coming from a given class, but they belong to another class. On the contrary, false negatives (FN) are the number of samples classified as belonging to a class that does not correspond to the true class. Accuracy, as depicted in (1), is a function of these quantities and it provides an intuitive way to determine which model is best at classifying input data. Furthermore, the better a model can generalize the ‘unseen’ data, the better predictions and insights it can produce. Precision, (2), quantifies the number of positive class predictions that belong to the positive class, while recall, as noted in (3), is the number of positive class predictions obtained from all positive examples in the dataset. The advantage of using precision and recall is that they can fairly describe classification performance also in presence of unbalanced datasets. Finally, the F-Measure in (4) provides a single score that balances both the concerns of precision and recall in one number.
In Figure 9, we can see the results in terms of the overall accuracy of the SqueezeNet, VGG-16, GoogLeNet, and AlexNet networks. The obtained results are very good, and all oscillate around 85–95%. Finally, in Table 4, we show the results in terms of precision, recall, and F-measure for each considered network.
Figure 9. Overall accuracy of DL Networks in object detection for SSA applications.
Table 4. Precision, Recall, and F-measure of DL Networks for SSA space debris detection.
The SqueezeNet was found to be the best-performing network in terms of overall accuracy. In addition to being a “squashed” network, therefore with much fewer parameters, it was the fastest to train. The AlexNet network follows in terms of performance. Using CNN with fewer layers has the advantage of lower hardware needs and shorter training times than VGG-16 and GoogLeNet. Indeed, shorter training times allow for testing more hyperparameters. This makes the entire training process easier. One of the key design choices of the VGG-16 network was to use smaller (3 × 3) convolutional filters than those of previous networks. This allows an accurate recognition of the images (with an overall accuracy of 87.50%, as we can see in Figure 9), at the cost of dramatically increasing the depth of the network. the VGG-16 model is an extremely heavy net (thanks also to its depth and the number of fully connected layers), characterized by the slowest training times. GoogLeNet network, although it has reached a good accuracy (85%), has turned out to be the least performing, but still faster than the VGG-16 (which exceeds by only 2% accuracy).

5. Conclusions and Future Directions

Space infrastructures are subject to collisions with debris, abandoned space objects, and other active satellites every day. This is a big problem as modern society relies heavily on space infrastructure for day-to-day operations, such as communication, guidance, navigation, weather forecasting, and spatial images. Therefore, being aware of the spatial situation and developing further new algorithms for the defense of space infrastructures is very important. This review paper focused on such dramatic issues, reviewing the recent papers published in the field of machine and deep learning for object detection in satellite-based Internet of Things (SIoT) networks. In particular, the first part of our paper discussed the importance of SIoT, explaining what it is, why it is important, and its main uses in everyday life. Then, we have illustrated the problem related to space debris and the reasons why the protection of satellites’ constellations is fundamental and necessary. The main methods of object detection were shown first without the use of deep learning frameworks and then with the use of CNN, which proved to be the best network to tackle this type of problem. In addition, innovative approaches were shown with other typologies of networks, discussing the use of Deep Reinforcement Learning. Finally, we conducted a comparison between several DL techniques for object detection in SSA scenarios via simulation results. Four different DL frameworks, namely Squeeze Net, Google Net, VGG-16, and Alex Net, were used for object detection for SSA applications. In particular, we simulated the use of a monostatic pulse radar that detects the radial velocity of moving targets at specific intervals. The results obtained through simulations demonstrate the efficiency of such methods for object detection in SSA scenarios, thus improving the SIoT network resilience to collisions and damages with space debris.
Radar and optical systems are both used in space surveillance. However, their performance and scope are very different. On the one hand, optical telescopes can observe objects at a great distance if the conditions are suitable and the angular velocities are low, but their efficiency highly depends on good weather conditions and adequate lighting during the night hours. On the other hand, radars are capable of tracking objects with higher angular velocities; they are available 24/7 and without restrictions of weather conditions. However, their sensitivity depends on the distance; radars are not very effective at tracking objects in higher positions compared to MEO (Medium Earth Orbit). Deep learning methods have been fully applied in radar detection. Using DL can not only process large amounts of data but also minimize or eliminate redundant and unimportant data. In addition, satellite imagery is ubiquitous in space, and networks such as CNN are especially good for working with imagery. Thus, characteristics that may not be identifiable to a human can be efficiently extracted by DL approaches. Furthermore, these approaches surpass traditional techniques in terms of accuracy. Not only DL models, such as radars, are robust to any meteorological change, but they also can work on any data, be it structured, unstructured, or semi-structured. The fusion of measurements from optical telescopes and the radar united with DL methodologies should provide highly acceptable orbit determination results because it is possible to combine the respective advantages and compensate for the respective disadvantages.
The authors believe that in the next future, unfortunately, the conflict between the required performance, the number of sensors, and the growing need for SSA data acquisition will continue to exist. The optimization of the system’s understanding capacity promises to reduce this conflict, as well as the multisource data fusion. Then, several important shortfalls must be counteracted in the next future, such as the need for large training datasets and very high training times. Hence, the designing of new classifiers is now moving towards the choice of exploiting heterogeneous data in small sample scenarios, thus reducing the training times and the requirements for the requested computational complexity. Finally, it’s the authors’ opinion that another problem that will be faced by researchers in the future would be the need to work with short monitoring intervals—being able to react quickly to debris decomposition for satellite maneuvers will be the next research topic area, thus improving the capacity of non-stop orbital prediction, tracking, and monitoring in hazardous situations. Hence, research will be conducted in order to strengthen the confidence of the system by improving early warning collision systems, as well as studying more accurate maneuvering avoidance policies.

Author Contributions

All authors have equally contributed to all the parts of this paper. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

No new data were created or analyzed in this study, Data sharing is not applicable to this article.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

AcronymExplanation
A3CActor-Critical Method
AIArtificial Intelligence
AIOTArtificial Intelligence of Things
AMIGOAdaptive Markov Inference Game Optimization
ANNArtificial Neural Network
AOTActive Object Tracking
ASPPA parallel Spatial Pyramid Pooling
CCTVClose-Circuit Television
CNNConvolutional Neural Network
DLDeep Learning
DNNDeep Neural Network
DRLDeep Reinforcement Learning
FCFully Connected
FCCFederal Communications Commission
FCNFully Convolutional Network
FFSMFloating Space Manipulators
FFTFast Fourier Transform
FGBNNFast Grid-Based Neural Network
FNNFuzzy Neural Network
FOVField of View
ILSVRCImageNet Large Scale Visual Recognition Challenge
IOTInternet of Things
ISSInternational Space Station
K-NNk-Nearest Neighbor
K-NN-DTWk-Nearest Neighbor combined with Dynamic Time Warping
LEOLow Earth Orbit
LSTMLong-Short Term Memory
MCNNTModified Convolutional Neural Networks Technique
MEOMedium Earth Orbit
MF-TBDMulti-Frame Track-Before-Detect
MLMachine Learning
PPOProximal Policy Optimization
PRFPulse Repetition Frequency
RCSRadar Cross Section
RELURectified Linear Unit
RLReinforcement Learning
RNNRecurrent Neural Network
ROIRegion of Interest
SDASpace Domain Awareness
SGDMStochastic Gradient Descent with Momentum
SIOTSatellite Internet of Things
SMSensor Management
SNRSignal to Noise Ratio
SSASpace Situational Awareness
STMSpace Traffic Management
SVMSupport Vector Machine
TDNNTime-Delay Neural Network
UAVUnmanned Aerial Vehicles

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