Space debris is internationally recognized as a planetary threat. Efforts to enhance the worldwide radar monitoring networks have been intensified in the last years. Among the new radars employed for the observations, one of the most promising is the Bistatic Radar for Low Earth Orbit (LEO) Tracking (BIRALET), which employs the Sardinia Radio Telescope as a receiving segment. The Sardinia Radio Telescope (SRT) has recently been proven to be a reliable instrument for space debris monitoring and, for this purpose, over the years has undergone some substantial modifications in order to be able to rise to the status of a fully functional radar receiver. However, an extensive measurement campaign, in order to assess the real potential of the radar, has never been done before. In this paper, the authors present the first real space debris measurement campaign of the SRT, made between December 2018 and October 2019 using the new dedicated channel of the P-band receiver. A total of 27 objects were correctly detected during this campaign, characterized by a radar cross section (RCS) interval between 0.13 and 13.4 m2 and a range interval between 459 and 1224 km.

Recent Advances of the BIRALET System about Space Debris Detection

Space debris detection over intersatellite communication signals

Hyperspectral remote sensing image (HSI) classification is very useful in different applications, and recently, deep learning has been applied for HSI classification successfully. However, the number of training samples is usually limited, causing difficulty in use of very deep learning models. We propose a wide and deep Fourier network to learn features efficiently by using pruned features extracted in the frequency domain. It is composed of multiple wide Fourier layers to extract hierarchical features layer-by-layer efficiently. Each wide Fourier layer includes a large number of Fourier transforms to extract features in the frequency domain from a local spatial area using sliding windows with given strides.These extracted features are pruned to retain important features and reduce computations. The weights in the final fully connected layers are computed using least squares. The transform amplitudes are used for nonlinear processing with pruned features. The proposed method was evaluated with HSI datasets including Pavia University, KSC, and Salinas datasets. The overall accuracies (OAs) of the proposed method can reach 99.77%, 99.97%, and 99.95%, respectively. The average accuracies (AAs) can achieve 99.55%, 99.95%, and 99.95%, respectively. The Kappa coefficients are as high as 99.69%, 99.96%, and 99.94%, respectively. The experimental results show that the proposed method achieved excellent performance among other compared methods. The proposed method can be used for applications including classification, and image segmentation tasks, and has the ability to be implemented with lightweight embedded computing platforms. The future work is to improve the method to make it available for use in applications including object detection, time serial data prediction, and fast implementation.

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Wide and Deep Fourier Neural Network for Hyperspectral Remote Sensing Image Classification

An algorithm was proposed to detect the space debris in the optical astronomical images. maximum projection was induced to reduce the data amount in the three dimensional space. when the space debris was detected, it was tracked by the particle filter thus the precise location information can be got at any time. the simulation results show that the method can perfectly meet the requirement of the space debris detection with a high accuracy (rms error less than 1 pixel) and detection probability, and a low false alarm rejection.

A maximum projection and particle filtering algorithm for space debris detection

Space Situational Awareness (SSA) data is critical to the safe piloting of satellites through an ever-growing field of orbital debris. However, measurement complexity means that most satellite operators cannot independently acquire SSA data and must rely on a handful of centralized repositories operated by major space powers. As interstate competition in orbit increases, so does the threat of attacks abusing these information-sharing relationships. This paper offers one of the first considerations of defense techniques against SSA deceptions. Building on historical precedent and real-world SSA data, we simulate an attack whereby an SSA operator seeks to disguise spy satellites as pieces of debris. We further develop and evaluate a machine-learning based anomaly detection tool which allows defenders to detect 90-98% of deception attempts with little to no in-house astrometry hardware. Beyond the direct contribution of this system, the paper takes a unique interdisciplinary approach, drawing connections between cyber-security, astrophysics, and international security studies. It presents the general case that systems security methods can tackle many novel and complex problems in an historically neglected domain and provides methods and techniques for doing so.

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On Detecting Deception in Space Situational Awareness

Space debris detection is important in space situation awareness and space asset protection. In this article, we propose a method to detect space debris using feature learning of candidate regions. The acquired optical image sequences are first processed to remove hot pixels and flicker noise, and the nonuniform background information is removed by the proposed one dimensional mean iteration method. Then, the feature learning of candidate regions (FLCR) method is proposed to extract the candidate regions and to detect space debris. The candidate regions of space debris are precisely extracted, and then classified by a trained deep learning network. The feature learning model is trained using a large number of simulated space debris with different signal to noise ratios (SNRs) and motion parameters, instead of using real space debris, which make it difficult to extract a sufficient number of real space debris with diverse parameters in optical image sequences. Finally, the candidate regions are precisely placed in the optical image sequences. The experiment is performed using the simulated data and acquired image sequences. The results show that the proposed method has good performance when estimating and removing background, and it can detect low SNR space debris with high detection probability.

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Space Debris Detection Using Feature Learning of Candidate Regions in Optical Image Sequences

Deep learning networks have achieved great success in many areas, such as in large-scale image processing. They usually need large computing resources and time and process easy and hard samples inefficiently in the same way. Another undesirable problem is that the network generally needs to be retrained to learn new incoming data. Efforts have been made to reduce the computing resources and realize incremental learning by adjusting architectures, such as scalable effort classifiers, multi-grained cascade forest (gcForest), conditional deep learning (CDL), tree CNN, decision tree structure with knowledge transfer (ERDK), forest of decision trees with radial basis function (RBF) networks, and knowledge transfer (FDRK). In this article, a parallel multistage wide neural network (PMWNN) is presented. It is composed of multiple stages to classify different parts of data. First, a wide radial basis function (WRBF) network is designed to learn features efficiently in the wide direction. It can work on both vector and image instances and can be trained in one epoch using subsampling and least squares (LS). Second, successive stages of WRBF networks are combined to make up the PMWNN. Each stage focuses on the misclassified samples of the previous stage. It can stop growing at an early stage, and a stage can be added incrementally when new training data are acquired. Finally, the stages of the PMWNN can be tested in parallel, thus speeding up the testing process. To sum up, the proposed PMWNN network has the advantages of: 1) optimized computing resources; 2) incremental learning; and 3) parallel testing with stages. The experimental results with the MNIST data, a number of large hyperspectral remote sensing data, and different types of data in different application areas, including many image and nonimage datasets, show that the WRBF and PMWNN can work well on both image and nonimage data and have very competitive accuracy compared to learning models, such as stacked autoencoders, deep belief nets, support vector machine (SVM), multilayer perceptron (MLP), LeNet-5, RBF network, recently proposed CDL, broad learning, gcForest, ERDK, and FDRK.

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Parallel Multistage Wide Neural Network

With the rapid development of research on machine learning models, especially deep learning, more and more endeavors have been made on designing new learning models with properties such as fast training with good convergence, and incremental learning to overcome catastrophic forgetting. In this paper, we propose a scalable wide neural network (SWNN), composed of multiple multi-channel wide RBF neural networks (MWRBF). The MWRBF neural network focuses on different regions of data and nonlinear transformations can be performed with Gaussian kernels. The number of MWRBFs for proposed SWNN is decided by the scale and difficulty of learning tasks. The splitting and iterative least squares (SILS) training method is proposed to make the training process easy with large and high dimensional data. Because the least squares method can find pretty good weights during the first iteration, only a few succeeding iterations are needed to fine tune the SWNN. Experiments were performed on different datasets including gray and colored MNIST data, hyperspectral remote sensing data (KSC, Pavia Center, Pavia University, and Salinas), and compared with main stream learning models. The results show that the proposed SWNN is highly competitive with the other models.

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Scalable Wide Neural Network: A Parallel, Incremental Learning Model Using Splitting Iterative Least Squares

Deep learning has flourished in large-scale supervised tasks. However, in many practical conditions, rich and available labeled data are a luxury. Thus, few-shot learning (FSL) has recently received boosting interest and achieved significant progress, which can learn new classes from several labeled samples. The advanced distribution calibration approach estimates the ground-truth distribution of few-shot classes by reusing the statistics of auxiliary data. However, there is still a significant discrepancy between the estimated distributions and ground-truth distributions, and artificially set hyperparameters cannot be adapted to different application scenarios (i.e., datasets). This paper proposes a prototype-based self-adaptive distribution calibration framework for estimating ground-truth distribution accurately and self-adaptive hyperparameter optimization for different application scenarios. Specifically, the proposed method is divided into two components. The prototype-based representative mechanism is for obtaining and utilizing more global information about few-shot classes and improving classification performance. The self-adaptive hyperparameter optimization algorithm searches robust hyperparameters for the distribution calibration of different application scenarios. The ablation studies verify the effectiveness of the various components of the proposed framework. Enormous experiments are conducted on three standard benchmarks such as miniImageNet, CUB-200-2011, and CIFAR-FS. The competitive results and compelling visualizations indicate that the proposed framework achieves state-of-the-art performance.

https://www.mdpi.com/2079-9292/12/1/134/pdf?version=1672215465

Prototype-Based Self-Adaptive Distribution Calibration for Few-Shot Image Classification

Neural Architecture Search (NAS) has recently shown a powerful ability to engineer networks automatically on various tasks. Most current approaches navigate the search direction with the validation performance-based architecture evaluation methodology, which estimates an architecture's quality by training and validating on a specific large dataset. However, for small-scale datasets, the model's performance on the validation set cannot precisely estimate that on the test set. The imprecise architecture evaluation can mislead the search to sub-optima. To address the above problem, we propose an efficient multi-objective evolutionary zero-shot NAS framework by evaluating architectures with zero-cost metrics, which can be calculated with randomly initialized models in a training-free manner. Specifically, a general zero-cost metric design principle is proposed to unify the current metrics and help develop several new metrics. Then, we offer an efficient computational method for multi-zero-cost metrics by calculating them in one forward and backward pass. Finally, comprehensive experiments have been conducted on NAS-Bench-201 and MedMNIST. The results have shown that the proposed method can achieve sufficiently accurate, high-throughput performance on MedMNIST and 20[Formula: see text]faster than the previous best method.

Xin Wei

Papers

Space Debris Detection Using Feature Learning of Candidate Regions in Optical Image Sequences

Parallel Multistage Wide Neural Network

Scalable Wide Neural Network: A Parallel, Incremental Learning Model Using Splitting Iterative Least Squares

Prototype-Based Self-Adaptive Distribution Calibration for Few-Shot Image Classification

An Efficient Multi-Objective Evolutionary Zero-Shot Neural Architecture Search Framework for Image Classification