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.

The number of resident space objects (RSOs) has been steadily increasing over time, posing significant risks to the safe operation of on-orbit assets. The accurate prediction of potential collision events and implementation of effective and nonredundant avoidance maneuvers require the precise estimation of the orbit positions of objects of interest and propagation of their associated uncertainties. Previous research mainly focuses on striking a balance between accurate propagation and efficient computation. A recently proposed approach that integrates uncertainty propagation with different coordinate representations has the potential to achieve such a balance. This paper proposes combining the generalized equinoctial orbital elements (GEqOE) representation with an adaptive Gaussian mixture model (GMM) for uncertainty propagation. Specifically, we implement a reformulation for the orbital dynamics so that the underlying state and the moment feature of the GMM are propagated under the GEqOE coordinates. Starting from an initial Gaussian probability distribution function (PDF), the algorithm iteratively propagates the uncertainty distribution using a detection-splitting module. A differential entropy-based nonlinear detector and a splitting library are utilized to adjust the number of GMM components dynamically. Component splitting is triggered when a predefined threshold of differential entropy is violated, generating several GMM components. The final probability density function (PDF) is obtained by a weighted summation of the component distributions at the target time. Benefiting from the nonlinearity reduction caused by the GEqOE representation, the number of triggered events largely decreases, causing the necessary number of components to maintain uncertainty realism also to decrease, which enables the proposed approach to achieve good performance with much more efficiency. As demonstrated by the results of propagation in three scenarios with different degrees of complexity, compared with the Cartesian-based approach, the proposed approach achieves comparable accuracy to the Monte Carlo method while largely reducing the number of components generated during propagation. Our results confirm that a judicious choice of coordinate representation can significantly improve the performance of uncertainty propagation methods in terms of accuracy and computational efficiency.

The atmosphere is a complex nonlinear system, with the information of its temperature, water vapor, pressure, and cloud being crucial aspects of remote-sensing data analysis. There exist intricate interactions among these internal components, such as convection, radiation, and humidity exchange. Atmospheric phenomena span multiple spatial and temporal scales, from small-scale thunderstorms to large-scale events like El Niño. The dynamic interactions across different scales, along with external disturbances to the atmospheric system, such as variations in solar radiation and Earth surface conditions, contribute to the chaotic nature of the atmosphere, making long-term predictions challenging. Grasping the intrinsic chaotic dynamics is essential for advancing atmospheric analysis, which holds profound implications for enhancing meteorological forecasts, mitigating disaster risks, and safeguarding ecological systems. To validate the chaotic nature of the atmosphere, this paper reviewed the definitions and main features of chaotic systems, elucidated the method of phase space reconstruction centered on Takens’ theorem, and categorized the qualitative and quantitative methods for determining the chaotic nature of time series data. Among quantitative methods, the Wolf method is used to calculate the Largest Lyapunov Exponents, while the G–P method is used to calculate the correlation dimensions. A new method named Improved Saturated Correlation Dimension method was proposed to address the subjectivity and noise sensitivity inherent in the traditional G–P method. Subsequently, the Largest Lyapunov Exponents and saturated correlation dimensions were utilized to conduct a quantitative analysis of FY-4A and Himawari-8 remote-sensing infrared observation data, and ERA5 reanalysis data. For both short-term remote-sensing data and long-term reanalysis data, the results showed that more than 99.91% of the regional points have corresponding sequences with positive Largest Lyapunov exponents and all the regional points have correlation dimensions that tended to saturate at values greater than 1 with increasing embedding dimensions, thereby proving that the atmospheric system exhibits chaotic properties on both short and long temporal scales, with extreme sensitivity to initial conditions. This conclusion provided a theoretical foundation for the short-term prediction of atmospheric infrared radiation field variables and the detection of weak, time-sensitive signals in complex atmospheric environments.

Infrared small target detection (IRSTD) supports remote-sensing surveillance and early warning, but infrared targets often occupy only a few pixels, exhibit weak appearance cues, and resemble thermal clutter. Vision-language detectors such as Grounding DINO provide a flexible prompt-driven detection interface, but direct transfer to IRSTD faces three mismatches: a single prompt cannot describe target appearance diversity, generic decoder queries are not calibrated for weak scale-sensitive targets, and IoU-style localization losses are unstable for few-pixel boxes. We propose PromptScaleDINO as a lightweight adaptation framework for Grounding DINO. It introduces an Anchor Prompt Bank (APB) to enrich the text input while keeping supervision connected to a stable anchor phrase, a Scale-Aware Query Refinement Network (SQRN) to refine selected queries according to predicted box scale and semantic confidence, and a geometry-aware localization objective that combines Normalized Wasserstein Distance (NWD) with Generalized Intersection over Union (GIoU). Under a unified detection protocol, PromptScaleDINO achieves its largest gains on the challenging real-scene IRSTD-1k benchmark while maintaining near-ceiling performance on SIRST and NUDT-SIRST. On IRSTD-1k, it reaches 88.32% F1 and 87.40% mAP@0.5, improving the baseline by 2.56 and 4.00 percentage points, respectively. These gains mainly reflect improved weak-target recovery at comparable precision; accordingly, we position the framework as a target-sensitivity and localization adaptation rather than an explicit false-alarm suppression method.

To address the problem of detecting dim small targets against complex moving backgrounds, based on spatiotemporal saliency features, this paper proposes a novel extraction model: the temporal and spatial saliency feature (TSSF) model. In the temporal domain processing stage, a temporal motion saliency operator is constructed by leveraging the differences in temporal motion responses between the target and the background; this operator simultaneously filters for candidate target points while suppressing large-area background clutter. In the spatial domain processing stage, a max-mean-based grayscale saliency operator is constructed to further validate the candidate target points. Building upon target enhancement processing, target trajectories can be effectively extracted through multi-frame association. Quantitative experiments on ten test sequences with diverse background complexities and signal-to-clutter ratios (SCRs) of around 1 or lower show that the TSSF model achieves optimal background suppression factors (BSFs) on seven sequences and the maximum SCR gain (SCRG) across eight sequences, with an average SCRG of roughly 330. For detection performance assessed via ROC curves, the proposed method obtains the highest AUC values on nine test sequences. The consistently superior performance validates the effectiveness and generalizability of the proposed TSSF model for dim small target enhancement, background suppression, and robust target detection.