Space Target Detection Research Articles

Image Dehazing Enhancement Strategy Based on Polarization Detection of Space Targets

In view of the fact that the technology of polarization detection performs better at identifying targets through clouds and fog, the recognition ability of the space target detection system under haze conditions will be improved by applying the technology. However, due to the low ambient brightness and limited target radiation information during space target detection, the polarization information of space target is seriously lost, and the advantages of polarization detection technology in identifying targets through clouds and fog cannot be effectively exerted under the condition of haze detection. In order to solve the above problem, a dehazing enhancement strategy specifically applied to polarization images of space targets is proposed. Firstly, a hybrid multi-channel interpolation method based on regional correlation analysis is proposed to improve the calculation accuracy of polarization information during preprocessing. Secondly, an image processing method based on full polarization information inversion is proposed to obtain the degree of polarization of the image after inversion and the intensity of the image after dehazing. Finally, the image fusion method based on discrete cosine transform is used to obtain the dehazing polarization fusion enhancement image. The effectiveness of the proposed image processing strategy is verified by carrying out simulated and real space target detection experiments. Compared with other methods, by using the proposed image processing strategy, the quality of the polarization images of space targets obtained under the haze condition is significantly improved. Our research results have important practical implications for promoting the wide application of polarization detection technology in the field of space target detection.

Aberration Modulation Correlation Method for Dim and Small Space Target Detection

The significance of detecting faint and diminutive space targets cannot be overstated, as it underpins the preservation of Earth’s orbital environment’s safety and long-term sustainability. Founded by the different response characteristics between targets and backgrounds to aberrations, this paper proposes a novel aberration modulation correlation method (AMCM) for dim and small space target detection. By meticulously manipulating the light path using a wavefront corrector via a modulation signal, the target brightness will fluctuate periodically, while the background brightness remains essentially constant. Benefited by the strong correlation between targets’ characteristic changes and the modulation signal, dim and small targets can be effectively detected. Rigorous simulations and practical experiments have validated the remarkable efficacy of AMCM. Compared to conventional algorithms, AMCM boasts a substantial enhancement in the signal-to-noise ratio (SNR) detection limit from 5 to approximately 2, with an area under the precision–recall curve of 0.9396, underscoring its ability to accurately identify targets while minimizing false positives. In essence, AMCM offers an effective method for detecting dim and small space targets and is also conveniently integrated into other passive target detection systems.

Doppler-Spread Space Target Detection Based on Overlapping Group Shrinkage and Order Statistics

Doppler-Spread Space Target Detection Based on Overlapping Group Shrinkage and Order Statistics

A Spatial Small Target Detection Method Based on a Multi-Scale Feature Fusion Pyramid Spatial Small Target Detection Method Based on Multi-scale Feature Fusion Pyramid

Small target detection has become an important part of space exploration missions. The existence of weak illumination and interference from the background of star charts in deep and distant space has brought great challenges to space target detection. In addition, the distance of space targets is usually far, so most of them are small targets in the image, and the detection of small targets is also very difficult. To solve the above problems, we propose a multi-scale feature fusion pyramid network. First, we propose the CST module of a CNN fused with Swin Transformer as the feature extraction module of the feature pyramid network to enhance the extraction of target features. Then, we improve the SE attention mechanism and construct the CSE module to find the attention region in the dense star map background. Finally, we introduce improved spatial pyramid pooling to fuse more features to increase the sensory field to obtain multi-scale object information and improve detection performance for small targets. We provide two versions and conducted a detailed ablation study to empirically validate the effectiveness and efficiency of the design of each component in our network architecture. The experimental results show that our network improved in performance compared to the existing feature pyramid.

Real-time hybrid method for maneuver detection and estimation of non-cooperative space targets

Real-time hybrid method for maneuver detection and estimation of non-cooperative space targets

A Space Target Detection Method Based on Spatial–Temporal Local Registration in Complicated Backgrounds

Human space exploration has brought a growing crowded operating environment for in-orbit spacecraft. Monitoring the space environment and detecting space targets with photoelectric equipment has extensive and realistic significance in space safety. In this study, a local spatial–temporal registration (LSTR) method is proposed to detect moving small targets in space. Firstly, we applied the local region registration to estimate the neighbor background motion model. Secondly, we analyzed the temporal local grayscale difference between the strong clutter and target region and measured the temporal local–central region difference to enhance the target. Then, the temporal pixel contrast map was calculated, which further retains the target signal and suppresses the residue clutter. Finally, a simple adaptive threshold segmentation algorithm was applied to the saliency map to segment the targets. Comparative experiments were conducted on four groups of image sequences to validate the efficiency and robustness of the algorithm. The experimental findings indicate that the proposed method performs well in target enhancement and clutter suppression under different scenarios.

GS-orthogonalization OMP method for space target detection via bistatic space-based radar

A space-based bistatic radar system composed of two space-based radars as the transmitter and the receiver respectively has a wider surveillance region and a better early warning capability for high-speed targets, and it can detect focused space targets more flexibly than the monostatic radar system or the ground-based radar system. However, the target echo signal is more difficult to process due to the high-speed motion of both space-based radars and space targets. To be specific, it will encounter the problems of Range Cell Migration (RCM) and Doppler Frequency Migration (DFM), which degrade the long-time coherent integration performance for target detection and localization inevitably. To solve this problem, a novel target detection method based on an improved Gram Schmidt (GS)-orthogonalization Orthogonal Matching Pursuit (OMP) algorithm is proposed in this paper. First, the echo model for bistatic space-based radar is constructed and the conditions for RCM and DFM are analyzed. Then, the proposed GS-orthogonalization OMP method is applied to estimate the equivalent motion parameters of space targets. Thereafter, the RCM and DFM are corrected by the compensation function correlated with the estimated motion parameters. Finally, coherent integration can be achieved by performing the Fast Fourier Transform (FFT) operation along the slow time direction on compensated echo signal. Numerical simulations and real raw data results validate that the proposed GS-orthogonalization OMP algorithm achieves better motion parameter estimation performance and higher detection probability for space targets detection.

Space Target Detection Based on DBF and GRFT for Ground-Based Distributed Radar

Space Target Detection Based on DBF and GRFT for Ground-Based Distributed Radar

A Novel Target Detection Method Based on Multi-Parameter Space for Mobile Passive Multistatic Radar

Deploying Passive Multistatic Radar (PMR) on mobile platforms provides covert and cost-effective monitoring over a large area, offering certain advantages in countermeasure. However, mobile PMR faces significant challenges, such as Doppler distortion and phase deviations. A multi-parameter space target detection method is proposed for mobile PMR to achieve target detection in three-dimensional environments. By estimating the Doppler Frequency Rate (DFR), applying bistatic range phase compensation, and implementing azimuth time integration, frame division, and data fusion, the detection accuracy and the Signal-to-Noise Ratio (SNR) are improved. Simulation results indicate that the proposed method significantly enhances the SNR and produces accurate detection results, demonstrating its efficacy.

Improvement of coated aluminum sheet pBRDF model based on scattering and phase function optimization

The pBRDF model is able to relate the properties of target materials to the polarization information of incident and reflected light, and is an important basis for obtaining polarization information of targets in space. It is an important basis for obtaining target polarization information and polarization detection of space targets. P-G model is the first strictly pBRDF model officially released, but there are still deficiencies. In this paper, we first analyze the assumption framework of the P-G model, derive the imperfections in the framework through the analysis of the assumption framework, and add scattering and phase function to enhance the existing model. On the basis of the existing P-G model and parameter inversion, the output results of the model are compared with the experimental data through simulation, and the results show that the relative error of the target's linear polarizability is reduced under the improved model, which proves the accuracy and precision of the improved model.

Infrared Dim Star Background Suppression Method Based on Recursive Moving Target Indication

Space-based infrared target detection can provide full-time and full-weather observation of targets, thus it is of significance in space security. However, the presence of stars in the background can severely affect the accuracy and real-time performance of infrared dim and small target detection, making star suppression a key technology and hot spot in the field of space target detection. The existing star suppression algorithms are all oriented towards the detection before track method and rely on the single image properties of the stars. They can only effectively suppress bright stars with a high signal-to-noise ratio (SNR). To address this problem, we propose a new method for infrared dim star background suppression based on recursive moving target indication (RMTI). Our proposed method is based on a more direct analysis of the image sequence itself, which will lead to more robust and accurate background suppression. The method first obtains the motion information of stars through satellite motion or key star registration. Then, the advanced RMTI algorithm is used to enhance the stars in the image. Finally, the mask of suppressing stars is generated by an accumulation frame adaptive threshold. The experimental results show that the algorithm has a less than 8.73% leakage suppression rate for stars with an SNR ≤ 2 and a false suppression rate of less than 2.3%. The validity of the proposed method is verified in real data. Compared with the existing methods, the method proposed in this paper can stably suppress stars with a lower SNR.

Space-Based THz Radar Fly-Around Imaging Simulation for Space Targets Based on Improved Path Tracing

Aiming at the space target detection application of a space-based terahertz (THz) radar, according to the imaging mechanism of broadband THz radars, a THz radar imaging simulation method based on improved path tracing is proposed. Firstly, the characterization method of THz scattering characteristics based on Kirchhoff’s approximation method is introduced. The multi-parameter THz bidirectional reflectance distribution function (THz-BRDF) models of aluminum (Al), white-painted Al, and polyimide film at 0.215 THz are fitted according to the theoretical data, with fitting errors below 4%. Then, the THz radar imaging simulation method based on improved path tracing is presented in detail. The simulation method utilizes path tracing to simulate parallelized THz radar echo signal data, considering multi-path energy scattering based on the THz-BRDF model. Finally, we conducted THz radar imaging simulation experiments. The influences in the imaging process of different fly-around motions are analyzed, and a comparison experiment is conducted with the fast-physical optics (FPO) method. The comparative results indicate that the proposed method exhibits richer and more realistic features compared with the FPO method. The simulation experiments results demonstrate that the proposed method can provide a data source for ground algorithm testing of THz radars, particularly in evaluating the target detection and recognition algorithm based on deep learning, providing strong support for the application of space-based THz radars in the future.

Design and Performance Analysis of the Highly Sensitive Deep Vacuum Cooling sCMOS Imaging System for Highly Sensitive Detection of Space Targets

The sCMOS imaging system with deep vacuum cooling technology has become a necessary way to improve the detection capability of space targets. In order to improve the detection capability of the photoelectric detection equipment for space targets, this paper developed the Highly Sensitive Deep Vacuum Cooling Imaging System (HSDVCIS). Firstly, we designed the imaging readout processing circuit using the GSENSE4040 sCMOS image sensor designed and manufactured by Gpixel and the deep vacuum cooling structure using thermoelectric cooling. Then, we tested the designed HSDVCIS with readout noise, dark current, and dynamic range of 3.96 e−, 0.12 e−/pixel/sec, and 84.49 dB, respectively, and tested the image sensor with a minimum cooling temperature of −40 °C. Finally, according to the results of observation experiments, we validated that the photoelectric detection equipment equipped with HSDVCIS improved the limiting detection magnitude (at SNR = 5 level) from 13.22 to 13.51 magnitudes within a 3 s exposure time by turning on the cooling function. Therefore, HSDVCIS designed in this paper can achieve highly sensitive detection of space targets. At the same time, the development of HSDVCIS also provides technical reserves and strong support for future research on the imaging systems using multiple image sensor mosaics.

Implementation of Real-Time Space Target Detection and Tracking Algorithm for Space-Based Surveillance

Space-based target surveillance is important for aerospace safety. However, with the increasing complexity of the space environment, the stellar target and strong noise interference pose difficulties for space target detection. Simultaneously, it is hard to balance real-time processing with computational performance for the onboard processing platform owing to resource limitations. The heterogeneous multi-core architecture has corresponding processing capabilities, providing a hardware implementation platform with real-time and computational performance for space-based applications. This paper first developed a multi-stage joint detection and tracking model (MJDTM) for space targets in optical image sequences. This model combined an improved local contrast method and the Kalman filter to detect and track the potential targets and use differences in movement status to suppress the stellar targets. Then, a heterogeneous multi-core processing system based on a field-programmable gate array (FPGA) and digital signal processor (DSP) was established as the space-based image processing system. Finally, MJDTM was optimized and implemented on the above image processing system. The experiments conducted with simulated and actual image sequences examine the accuracy and efficiency of the MJDTM, which has a 95% detection probability while the false alarm rate is 10−4. According to the experimental results, the algorithm hardware implementation can detect targets in an image with 1024 × 1024 pixels in just 22.064 ms, which satisfies the real-time requirements of space-based surveillance.

Dim and Small Space-Target Detection and Centroid Positioning Based on Motion Feature Learning

The detection of dim and small space-targets is crucial in space situational awareness missions; however, low signal-to-noise ratio (SNR) targets and complex backgrounds pose significant challenges to such detection. This paper proposes a space-target detection framework comprising a space-target detection network and a k-means clustering target centroid positioning method. The space-target detection network performs a three-dimensional convolution of an input star image sequence to learn the motion features of the target, reduces the interference of noise using a soft thresholding module, and outputs the target detection result after positioning via the offsetting branch. The k-means centroid positioning method enables further high-precision subpixel-level centroid positioning of the detection network output. Experiments were conducted using simulated data containing various dim and small space-targets, multiple noises, and complex backgrounds; semi-real data with simulated space-targets added to the real star image; and fully real data. Experiments on the simulated data demonstrate the superior detection performance of the proposed method for multiple SNR conditions (particularly with very low false alarm rates), robustness regarding targets of varying numbers and speeds, and complex backgrounds (such as those containing stray light and slow motion). Experiments performed with semi-real and real data both demonstrate the excellent detection performance of the proposed method and its generalization capability.

Small Space Target Detection Based on a Convolutional Neural Network and Guidance Information

Although space targets have different shapes, sizes and intensities, their distributions share certain commonalities. However, it is difficult to summarize a generalized distribution function for space targets. Moreover, most of the existing methods based on deep learning are not suitable to use directly because of the size of targets and the cost of manual labeling for a full image. In this paper, we proposed a pattern for space target detection based on a convolutional neural network (CNN) to learn essential features of the targets from data. In the processing stage, the background is estimated and removed. Then, image techniques are used to search and process region proposals. Different sizes of region proposals are recognized by a discriminator, which is built upon a small CNN trained with the data of several specific targets. Finally, a non-maximum suppression (NMS) operation is used to remove redundant targets. In the network structure, to further enhance the influence of the effective area, the parameters calculated from the center region of the input are utilized as guidance information and added to the features before the full connection. Moreover, the bias loss is applied to increase the weights of unique features. The experimental results demonstrate the outstanding performance of the proposed method in terms of the number of detected targets, accuracy rate and false alarm rate compared with baseline methods. In particular, the proposed method has a simple network structure and a lower computational cost which can be further promoted and implemented in actual engineering.

Non-Cooperative Target Attitude Estimation Method Based on Deep Learning of Ground and Space Access Scene Radar Images

Determining the attitude of a non-cooperative target in space is an important frontier issue in the aerospace field, and has important application value in the fields of malfunctioning satellite state assessment and non-cooperative target detection in space. This paper proposes a non-cooperative target attitude estimation method based on the deep learning of ground and space access (GSA) scene radar images to solve this problem. In GSA scenes, the observed target satellite can be imaged not only by inverse synthetic-aperture radar (ISAR), but also by space-based optical satellites, with space-based optical images providing more accurate attitude estimates for the target. The spatial orientation of the intersection of the orbital planes of the target and observation satellites can be changed by fine tuning the orbit of the observation satellite. The intersection of the orbital planes is controlled to ensure that it is collinear with the position vector of the target satellite when it is accessible to the radar. Thus, a series of GSA scenes are generated. In these GSA scenes, the high-precision attitude values of the target satellite can be estimated from the space-based optical images obtained by the observation satellite. Thus, the corresponding relationship between a series of ISAR images and the attitude estimation of the target at this moment can be obtained. Because the target attitude can be accurately estimated from the GSA scenes obtained by a space-based optical telescope, these attitude estimation values can be used as training datasets of ISAR images, and deep learning training can be performed on ISAR images of GSA scenes. This paper proposes an instantaneous attitude estimation method based on a deep network, which can achieve robust attitude estimation under different signal-to-noise ratio conditions. First, ISAR observation and imaging models were created, and the theoretical projection relationship from the three-dimensional point cloud to the ISAR imaging plane was constructed based on the radar line of sight. Under the premise that the ISAR imaging plane was fixed, the ISAR imaging results, theoretical projection map, and target attitude were in a one-to-one correspondence, which meant that the mapping relationship could be learned using a deep network. Specifically, in order to suppress noise interference, a UNet++ network with strong feature extraction ability was used to learn the mapping relationship between the ISAR imaging results and the theoretical projection map to achieve ISAR image enhancement. The shifted window (swin) transformer was then used to learn the mapping relationship between the enhanced ISAR images and target attitude to achieve instantaneous attitude estimation. Finally, the effectiveness of the proposed method was verified using electromagnetic simulation data, and it was found that the average attitude estimation error of the proposed method was less than 1°.

Lightweight and high-sensitive optical camera technology for faint space target detection

Lightweight and high-sensitive optical camera technology for faint space target detection

Reflective Tomography Lidar Image Reconstruction for Long Distance Non-Cooperative Target

In the long-distance space target detection, the technique of laser reflection tomography (LRT) shows great power and attracts more attention for further study and real use. However, space targets are often non-cooperative, and normally a 360° complete view of reflection projections cannot be obtained. Therefore, this article firstly introduces an improved LRT system design with more advanced laser equipment for long-distance non-cooperative detection to ensure the high quality of the lidar beam and the lidar projection data. Then, the LRT image reconstruction is proposed and focused on the laser image reconstruction method utilizing the total variation (TV) minimization approach based on the sparse algebraic reconstruction technique (ART) model, in order to reconstruct the laser image in a sparse or incomplete view of projections. At last, comparative experiments with the system are performed to validate the advantages of this method with the LRT system. In both near and far field experiments, the effectiveness and superiority of the proposed method are verified for different types of projection data through comparison to typical methods.

A Novel Space-Borne High-Resolution SAR System with the Non-Uniform Hybrid Sampling Technology for Space Targets Imaging

In order to reduce the complexity of the receiving system for wideband signals, a novel space-borne high-resolution synthetic aperture radar (SAR) system with the non-uniform hybrid sampling technology is proposed in this paper. The non-uniform hybrid sampling technology is firstly applied in the SAR imaging system for the detection of space targets. The non-uniform hybrid sampling technology is able to optimize the transmitted and receiving timing of SAR signals, reducing the requirement of the sampling rate of the analog-to-digital converter (ADC) for the reception of the wideband echoes from space targets. Meanwhile, according to the oversampling requirement of SAR imaging in the azimuth direction, a theoretical model of non-uniform hybrid sampling parameters and relative velocity between the SAR system and the space targets is established. A series of simulation experiments with different targets and different non-uniform hybrid parameters are performed, and an X-band SAR imaging experimental system is constructed to verify the effectiveness of the proposed non-uniform hybrid sampling technology. The experimental results show that the imaging resolution is better than 8 cm. When the non-uniform hybrid sampling interval is 15 us, the imaging quality is consistent with imaging results of the Nyquist real-time sampling, and it is easier to implement in the high-resolution imaging for space targets.