中国人民解放军空军预警学院

To address the issue of neglecting scenarios involving joint operations and collaborative drone swarm operations in air combat target intent recognition. This paper proposes a transfer learning-based intention prediction model for drone formation targets in air combat. This model recognizes the intentions of multiple aerial targets by extracting spatial features among the targets at each moment. Simulation results demonstrate that, compared to classical intention recognition models, the proposed model in this paper achieves higher accuracy in identifying the intentions of drone swarm targets in air combat scenarios.

In this paper, we investigate the adaptive detection of range-distributed targets in compound-Gaussian clutter, where the texture component follows a Weighted Generalized Inverse Gaussian (WGIG) distribution. We propose adaptive detectors for WGIG-distributed clutter based on two-step Rao, Wald, Durbin, and Gradient tests. The unknown covariance matrix is estimated using Approximate Maximum Likelihood (AML) and the Normalized Sample Covariance Matrix (NSCM). To address the analytical intractability of Maximum A Posteriori (MAP) estimation for the texture component, we adopt an alternative approach: The MAP estimator of the reciprocal expectation of the texture component, which is used in designing adaptive detectors based on the Rao, Wald, and Durbin tests. For the Gradient test-based detector, the test statistic is derived directly from the posterior probability density function. Our theoretical analysis confirms the consistency of the detectors derived from the Rao, Durbin, and Gradient tests. Extensive evaluations on both simulated and real data yield three key findings: (1) The proposed AML-based detectors maintain the constant false alarm rate property; (2) Under matched signal conditions, the detectors based on the Rao and Wald tests achieve the best performance on both the IPIX radar dataset and the Journal of Radar’s maritime surveillance dataset—specifically, they outperform the two-step generalized likelihood ratio test-based detector, requiring 0.1～0.5 dB and 0.7～0.8 dB lower Signal-to-Clutter Ratio (SCR) to achieve the same detection probability, respectively; and (3) Under mismatched signal conditions, the Rao test-based detector with AML estimation exhibits superior robustness, while the Wald test-based detector demonstrates the strongest suppression capability against mismatched signals.

The paper proposes a double hierarchical nonhomogeneous multirank target detection method for the distributed Multiple-Input Multiple-Output (MIMO) radar to detect targets in scenarios with subspace interference and nonhomogeneous clutter. First, a multirank target model and subspace interference model are established based on the fact that the target signal and interference are located in two linearly independent subspaces, each with a rank greater than 1. The corresponding subspace matrices of the two subspaces and the coordinate vectors of the corresponding distance units are unknown. Then, a distributed MIMO radar system with a double hierarchical nonhomogeneous structure is designed, and the interference of each transmit-receive pair is nonhomogeneous, i.e., each transmit-receive pair possesses different statistics. In addition, the clutter in one transmit-receive pair is nonhomogeneous. Subsequently, the double hierarchical nonhomogeneous multirank target Rao detector and Wald detector are designed for the distributed MIMO radar in the context of subspace interference. This is achieved by adopting the Rao and Wald test criteria, constructing the parameter estimation strategy to be solved, and estimating the power median normalized covariance. Theoretical derivation showed that the proposed method had a constant false alarm property for the clutter covariance matrix structure. Simulation experiments showed that the proposed method guarantees a constant false alarm property for the clutter covariance matrix structure; in addition, compared with the existing distributed MIMO radar detection methods, the proposed detection method improves the target detection and interference suppression performances.

With the deep integration of remote sensing technology and artificial intelligence,the human demand for the application of remote sensing data has become increasingly refined.However,single-modal data has limitations in the interpretation of complex scenes.Optical imagery,while rich in spatial information,suffers from weather dependencies;Synthetic Aperture Radar(SAR)data provides all-weather capability but lacks intuitive interpretability;and hyperspectral data,despite its detailed spectral signatures,presents challenges in data redundancy and computational complexity.Therefore,single-modal data is difficult to fully exploit the deeper information in remote sensing images.These limitations underscore the critical need for more advanced analytical approaches that can overcome the constraints of single-modal data interpretation. For this reason,the collaborative analysis of multi-modal data has become a key way to enhance the interpretation capability of remote sensing and is driving the further development of the remote sensing field.By integrating complementary data sources and leveraging their synergistic relationships,multi-modal approaches enable more robust and comprehensive scene interpretation.Among various multi-modal strategies,remote sensing image-text cross-modal understanding has gained particular prominence as it establishes a vital connection between remote sensing image features and human semantic cognition.This framework enhances visual feature representations with the help of text semantic information and achieves cross-modal information complementarity,which significantly improves the performance of remote sensing interpretation.This paper provides a comprehensive examination of remote sensing image-text cross-modal understanding,which is categorized into four main types of tasks:remote sensing image captioning(referred to as image-to-text),text-to-image generation(referred to as text-to-image),remote sensing image-text alignment(referred to as image-text alignment),and remote sensing visual question answering(referred to as image-text dialogue).From the perspective of cross-modal transformation of images and text,remote sensing image-text cross-modal understanding includes image-to-text and text-to-image.In terms of local content interaction,remote sensing image-text cross-modal understanding includes image-text alignment and image-text dialogue. This paper begins with a thorough review of the historical development and current state of image-text cross-modal research.The research progress at home and abroad of four tasks,namely image-to-text,text-to-image,image-text alignment,and image-text dialogue,is reviewed.The key technological breakthroughs in the fields of image-text cross-modal are mainly introduced.On this basis,the in-depth analysis is conducted on the technical difficulties faced by the four tasks.Then,it presents a detailed analysis of commonly used public datasets and evaluation metrics for remote sensing image-text cross-modal understanding.Finally,it summarizes the technical challenges in this field,which mainly include three aspects:modal alignment of remote sensing images and text,cross-modal interpretability,and cross-modal reasoning.Based on the proposed problem of remote sensing image-text cross-modal,it looks forward to the future research directions.(1)In-depth mining of cross-modal information(2)Construction of earth science knowledge graph.(3)Human-computer interaction.(4)Large-scale remote sensing image-text model.(5)Language diversity.(6)Remote sensing multi-source data. This comprehensive investigation not only synthesizes current research but also provides a clear roadmap for future research in remote sensing image-text cross-modal understanding,with potential implications for numerous practical applications in environmental monitoring,urban planning,and disaster management.

On the issue of how to effectively utilize radar resources to improve the accuracy and reliability of target detection,proposed a hybrid distributed phased array-multiple input and multiple output(PA-MIMO)radar system model and established a target detection model based on the Neyman-Pearson(NP)criterion.The likelihood ratio(LRT)detector was derived under the condition of channel reciprocity caused by sub-array transceiver sharing and multi-pulse coherent accumulation,and the target detection probability under a given false alarm probability was obtained.On this basis,by comparing the performances of phased array radar,single input and multiple output(SIMO)radar,multiple input and single output(MISO)radar,and PA-MIMO radar with optimal array element configuration,it was verified that the hybrid distributed PA-MIMO radar system utilized the correlation of signals within sub-arrays and the independence of signals between sub-arrays to simultaneously obtain coherent processing gain and waveform diversity gain,and its maximum detection probability reached 0.987 4,which was higher than the other four radar systems,effectively improving the target detection performance of the radar system.Finally,the effect of channel reciprocity and pulse accumulation on the radar detection performance was verified by simulation analysis.