In the actual tracking scenarios of the CMIMO (collocated multiple-input multiple-output) radar, the high dynamic RCS (radar cross section) fluctuation characteristic is not utilized effectively and thus it will lead to low tracking accuracy or even missing tracking. To solve this problem, a CMIMO radar power resource adaptive allocation method based on the high dynamic RCS fluctuation characteristic was proposed. Note that the target RCS was sensitive to the observing angle and the actual observing angle could be obtained dynamically via the prediction of target kinetic state, thus the polarization mode could be optimized during different tracking frames. Thereafter, the tracking posterior Cramer-Rao bound which included radar transmitting power and RCS was derived and it could see as the object function to be optimized. Finally, the internal penalty function method was implemented to tackle the aforementioned optimization problem and it achieved the optimized power allocation with high dynamic RCS. Simulation results validate that compared with the traditional RCS model allocation method, the proposed method fully utilizes the dynamic RCS fluctuation characteristics to achieve the effective allocation and it solves the mismatched problem between the allocation scheme and the actual tracking scenarios, which improves the multi-target tracking performance of the CMIMO radar.

In the few-shot recognition scenario of space targets observed at low frequency, the drastic changes in the image representation of space targets in different poses challenges to the extraction of discriminative features and the correlation of features between images. To address these issues, the few-shot space target recognition method based on adaptive cross fusion of local features was proposed. Based on the existing few-shot learning framework, the feature cross fusion module based on self-attention and cross-attention was used to adaptively learn the correlation between local features, improve the discriminant and robustness of feature in different poses, effectively explore the similarity between the support set and the query set, and improve the accuracy of feature association with representation differences. Meanwhile, the sample label weight based on neighborhood density was employed into the loss function to solve the learning bias problem of the network model caused by unbalanced space target datasets. Through the verification on different datasets, the proposed method is proved to achieve higher recognition accuracy.

Pointing estimation for spacecraft using Inverse Synthetic Aperture Radar (ISAR) images plays a significant role in space situational awareness and surveillance. However, feature extraction and cross-range scaling of ISAR images create bottlenecks that limit performances of current estimation methods. Especially, the emergence of staring imaging satellites, characterized by complex kinematic behaviors, presents a novel challenge to this task. To address these issues, this article proposes a pointing estimation method based on Convolutional Neural Networks (CNNs) and a numerical optimization algorithm. A satellite’s main axis, which is extracted from ISAR images by a proposed Semantic Axis Region Regression Net (SARRN), is chosen for investigation in this article due to its unique structure. Specifically, considering the kinematic characteristic of the staring satellite, an ISAR imaging model is established to bridge the target pointing and the extracted axes. Based on the imaging model, pointing estimation and cross-range scaling can be described as a maximum likelihood estimation problem, and an iterative optimization algorithm modified by using the strategy of random sampling-consistency check and weighted least squares is proposed to solve this problem. Finally, the pointing of targets and the cross-range scaling factors of ISAR images are obtained. Simulation experiments based on actual satellite orbital parameters verify the effectiveness of the proposed method. This work can improve the performance of satellite reconnaissance warning, while accurate cross-range scaling can provide a basis for subsequent data processes such as 3D reconstruction and attitude estimation.