It is of great significance to apply the object detection methods to automatically detect boulders from planetary images and analyze their distribution. This contributes to the selection of candidate landing sites and the understanding of the geological processes. This paper improves the state-of-the-art object detection method of YOLOv5 with attention mechanism and designs a pyramid based approach to detect boulders from planetary images. A new feature fusion layer has been designed to capture more shallow features of the small boulders. The attention modules implemented by combining the convolutional block attention module (CBAM) and efficient channel attention network (ECA-Net) are also added into YOLOv5 to highlight the information that contribute to boulder detection. Based on the Pascal Visual Object Classes 2007 (VOC2007) dataset which is widely used for object detection evaluations and the boulder dataset that we constructed from the images of Bennu asteroid, the evaluation results have shown that the improvements have increased the performance of YOLOv5 by 3.4% in precision. With the improved YOLOv5 detection method, the pyramid based approach extracts several layers of images with different resolutions from the large planetary images and detects boulders of different scales from different layers. We have also applied the proposed approach to detect the boulders on Bennu asteroid. The distribution of the boulders on Bennu asteroid has been analyzed and presented.

Improving YOLOv5 with Attention Mechanism for Detecting Boulders from Planetary Images

Non-geostationary (NGSO) satellites are envisioned to support various new communication applications from countless industries. NGSO systems are known for a number of key features such as lower propagation delay, smaller size, and lower signal losses in comparison to the conventional geostationary (GSO) satellites, which will enable latency-critical applications to be provided through satellites. NGSO promises a dramatic boost in communication speed and energy efficiency, and thus, tackling the main inhibiting factors of commercializing GSO satellites for broader utilizations. However, there are still many NGSO deployment challenges to be addressed to ensure seamless integration not only with GSO systems but also with terrestrial networks. These unprecedented challenges are discussed in this paper, including coexistence with GSO systems in terms of spectrum access and regulatory issues, satellite constellation and architecture designs, resource management problems, and user equipment requirements. Beyond this, the promised improvements of NGSO systems have motivated this survey to provide the state-of-the-art NGSO research focusing on the communication prospects, including physical layer and radio access technologies along with the networking aspects and the overall system features and architectures. We also outline a set of innovative research directions and new opportunities for future NGSO research.

A Survey on Non-Geostationary Satellite Systems: The Communication Perspective

/pdf/deep-learning-based-spacecraft-relative-navigation-methods-a-2qdzi8yj.pdf

Deep learning-based spacecraft relative navigation methods: A survey

This article presents a novel deep learning method for semi-automated detection of historic mining pits using aerial LiDAR data. The recent emergence of national scale remotely sensed datasets has created the potential to greatly increase the rate of analysis and recording of cultural heritage sites. However, the time and resources required to process these datasets in traditional desktop surveys presents a near insurmountable challenge. The use of artificial intelligence to carry out preliminary processing of vast areas could enable experts to prioritize their prospection focus; however, success so far has been hindered by the lack of large training datasets in this field. This study develops an innovative transfer learning approach, utilizing a deep convolutional neural network initially trained on Lunar LiDAR datasets and reapplied here in an archaeological context. Recall rates of 80% and 83% were obtained on the 0.5 m and 0.25 m resolution datasets respectively, with false positive rates maintained below 20%. These results are state of the art and demonstrate that this model is an efficient, effective tool for semi-automated object detection for this type of archaeological objects. Further tests indicated strong potential for detection of other types of archaeological objects when trained accordingly.

https://www.mdpi.com/2072-4292/11/17/1994/pdf

Bringing Lunar LiDAR Back Down to Earth: Mapping Our Industrial Heritage through Deep Transfer Learning

It is often necessary to identify a pattern of observed craters in a single image of the lunar surface and without any prior knowledge of the camera's location. This so-called "lost-in-space" crater identification problem is common in both crater-based terrain relative navigation (TRN) and in automatic registration of scientific imagery. Past work on crater identification has largely been based on heuristic schemes, with poor performance outside of a narrowly defined operating regime (e.g., nadir pointing images, small search areas). This work provides the first mathematically rigorous treatment of the general crater identification problem. It is shown when it is (and when it is not) possible to recognize a pattern of elliptical crater rims in an image formed by perspective projection. For the cases when it is possible to recognize a pattern, descriptors are developed using invariant theory that provably capture all of the viewpoint invariant information. These descriptors may be pre-computed for known crater patterns and placed in a searchable index for fast recognition. New techniques are also developed for computing pose from crater rim observations and for evaluating crater rim correspondences. These techniques are demonstrated on both synthetic and real images.

Lunar Crater Identification in Digital Images

The detection and identification of impact craters on a planetary surface are crucially important for planetary studies and autonomous navigation. Crater detection refers to finding craters in a given image, whereas identification means to actually mapping them to particular reference craters. However, no method is available for simultaneously detecting and identifying craters with sufficient accuracy and robustness. Thus, this study proposes a novel end-to-end fully convolutional neural network (CNN), namely, CraterIDNet, which takes remotely sensed planetary images of any size as input and outputs detected crater positions, apparent diameters, and identification results. CraterIDNet comprises two pipelines, namely, crater detection pipeline (CDP) and crater identification pipeline (CIP). First, we propose a pre-trained model with high generalization performance for transfer learning. Then, anchor scale optimization and anchor density adjustment are proposed for CDP. In addition, multi-scale impact craters are detected simultaneously by using different feature maps with multi-scale receptive fields. These strategies considerably improve the detection performance of small craters. Furthermore, a grid pattern layer is proposed to generate grid patterns with rotation and scale invariance for CIP. The grid pattern integrates the distribution and scale information of nearby craters, which will remarkably improve identification robustness when combined with the CNN framework. We comprehensively evaluate CraterIDNet and present state-of-the-art crater detection and identification performance with a small network architecture (4 MB).

CraterIDNet: An End-to-End Fully Convolutional Neural Network for Crater Detection and Identification in Remotely Sensed Planetary Images

In this paper an adaptive narrow-band interference (NBI) suppression scheme based on a code-aided technique in the global navigation satellite system inter-satellite links (ISLs) is proposed. Unlike the commonly used infinte impulse response (IIR) notch filter and frequency domain filtering, the code-aided technique does not require interference detection or fast Fourier transforms, and the adaptive performance is better. We introduce the principle and implementation of code-aided technique, and the concept of interference influence coefficient is proposed to evaluate the influence of residual interference on the carrier-to-noise ratio (CNR) at the receiving end. Through simulation and by combining the tracking CNR after antijamming, the optimal antijamming parameters and scheme are obtained. Meanwhile, we analyze the relationship between the loss of CNR caused by residual interference and the interference influence coefficient. The final results of the bit error rate (BER) and ranging error in ISLs indicate that the adaptive interference suppression scheme based on the code-aided technique can effectively suppress NBI and ensure the reliability of ISL communications. In the case where the pseudo code rate is 10.23 MHz and the BER does not exceed $1 \times {10^{ - 5}}$ , for the NBI with a bandwidth of 2 MHz, the maximum interference-to-signal ratio (ISR) that can be suppressed by the proposed scheme is 40 dB. For the NBI with a bandwidth of 1 MHz, the maximum ISR that can be suppressed is 55 dB, and for single frequency interference, the maximum ISR that can be suppressed is 75 dB.

Adaptive Narrow-Band Interference Suppression and Performance Evaluation Based on Code-Aided in GNSS Inter-Satellite Links

When a global navigation satellite system (GNSS) receiver suppresses interference in a dynamic scene, the direction of the interference signal arriving at the receiver may change rapidly. The null formed by the spatial filtering based on the array antenna will become wider and shallower, and the anti-jamming performance will deteriorate. A null-steering control technique based on a dual projection algorithm is proposed in this paper, which can effectively increase the depth of the null. In this paper, the dynamic model between the interference and the receiver is established first. Based on the model, the rate of change of the arrival direction of the interference is analyzed, and the phenomenon of the spatial filtering null becoming wider and shallower is simulated and verified. Then, the double projection algorithm is introduced to effectively deepen the null. The simulation results show that the proposed method can effectively increase the null depth by 30 to 50 dB, which significantly improves the anti-jamming performance of the spatial filtering in dynamic scenes.

An Anti-Jamming Null-Steering Control Technique Based on Double Projection in Dynamic Scenes for GNSS Receivers

The simultaneous extraction of optical navigation measurements from a target celestial body and star images is essential for autonomous optical navigation. Generally, a single optical navigation sensor cannot simultaneously image the target celestial body and stars well-exposed because their irradiance difference is generally large. Multi-sensor integration or complex image processing algorithms are commonly utilized to solve the said problem. This study analyzes and demonstrates the feasibility of simultaneously imaging the target celestial body and stars well-exposed within a single exposure through a single field of view (FOV) optical navigation sensor using the well capacity adjusting (WCA) scheme. First, the irradiance characteristics of the celestial body are analyzed. Then, the celestial body edge model and star spot imaging model are established when the WCA scheme is applied. Furthermore, the effect of exposure parameters on the accuracy of star centroiding and edge extraction is analyzed using the proposed model. Optimal exposure parameters are also derived by conducting Monte Carlo simulation to obtain the best performance of the navigation sensor. Finally, laboratorial and night sky experiments are performed to validate the correctness of the proposed model and optimal exposure parameters.

https://www.mdpi.com/1424-8220/17/4/915/pdf

Celestial Object Imaging Model and Parameter Optimization for an Optical Navigation Sensor Based on the Well Capacity Adjusting Scheme

In the estimation of the direction of arrival (DOA) for interference signals, the direction-finding error of the multiple signal classification (MUSIC) algorithm will increase in the case of multiple interferences or when the interfering signal power is weak. In this paper, a space-time conversion MUSIC (STC-MUSIC) algorithm is proposed, and the concept of a focusing parameter is introduced to improve the performance of the DOA estimation. Meanwhile, a method of variable step size peak search is proposed to reduce the amount of calculation of the STC-MUSIC algorithm. The final simulation and experimental results show that the STC-MUSIC algorithm improves the purity of the noise subspace effectively, thus improving the precision and robustness of the DOA estimation for interference signals significantly. In comparison to traditional algorithms, the convergence, stability, root mean square error (RMSE) and other performance characteristics are improved greatly.

Estimation of Interference Arrival Direction Based on a Novel Space-Time Conversion MUSIC Algorithm for GNSS Receivers.

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