Hypersonic targets have complex motion states and high maneuverability. The traditional interactive multi-model (IMM) has low tracking accuracy and a slow convergence speed. Therefore, this paper proposes a strong tracking cubature Kalman filter (CKF) adaptive interactive multi-model (AIMM) based on multiple fading factors. Firstly, this paper analyzes the structure of the CKF algorithm, introduces the fading factor of the strong tracking algorithm into the covariance matrix of the time update and measurement update, and adjusts the filter gain online and in real time, which can reduce the decline infilter accuracy caused by model mismatch. Secondly, Singer model, “current” statistical (CS) model, and Jerk model are selected in the model set of IMM and introduced singular value decomposition (SVD) decomposition to solve the problem that Cholesky decomposition cannot be performed in the CKF due to the model dimension expansion. Last, an adaptive algorithm for the Markov matrix in the IMM is proposed. The transition probability was adaptively modified by the value of the model likelihood function to enhance the proportion of matching models. The simulation results show that the proposed algorithm enhanced the proportion of matching models in the IMM and improved the tracking accuracy by 16.51% and the convergence speed by 37.5%.

Rotating object detection in remote sensing image has made substantial progress. However, the widely used feature extraction and fusion modules cannot accurately represent the spatial location and angle information of remote sensing objects in arbitrary orientation. A detection scheme that accurately expresses spatial angle characteristics is necessary when remote sensing objects are densely distributed. This paper proposes an effective network for rotating object detection in optical remote sensing image (BDR-Net). Firstly, a feature aggregation module (FAM) is designed to refine the feature extraction of rotating objects, which can generate refined feature maps. Secondly, two-dimensional (2-D) Gaussian modeling simulates the rotation angle information of the object. Thirdly, given that the Bhattacharyya distance is mathematically symmetric and rigorous, we design a Bhattacharyya distance based learning module (BDLM). We improve the loss function to more accurately guide the regression of the relative location information of a rotating box. The effectiveness of the proposed method is validated by the results of experiments on three public datasets: DOTA, DIOR, and HRSC2016. The results of our research prove that a Bhattacharyya distance with a Gaussian distribution could be used as an approximate regression loss of the object detection model. This paper offers an original approach to combining object detection with metric learning to apply rotating object detection to instrumentation in a deployable and rapid manner.

Given the susceptibility of satellite signals to intentional or unintentional interference; it is imperative to incorporate hardware-based navigation anti-jamming algorithms. Currently, spatial domain filtering is widely applied. While numerous variable step size (VSS) algorithms were used to improve the performance, most of these are either computationally complex or depend on many parameters which are hard to tune manually. Some algorithms might improve one index but worsen overall performance, making them unreliable in practical scenarios. To address these challenges, we propose a novel variable step power inversion (PI) that involves just one adaptive parameter. By establishing a new non-linear function model between step size and system output, the parameter can be adapted based on the output. Meanwhile, a variable step PI that can be easily implemented by field programmable gate array (FPGA) are proposed, thereby achieving a balance between accuracy and iteration speed. The proposed algorithm can converge within 10 iterations, effectively suppressing individual interference to below -300dB. In comparison to existing algorithms, the proposed algorithm is characterized by swift convergence, high convergence accuracy, low complexity, and robust anti-jamming capabilities. Finally, FPGA hardware testing are conducted, and the experimental and simulation results are in good agreement, affirming anti-jamming capabilities.

In this paper, a satellite attitude estimation algorithm based on Strong Tracking Robust Adaptive Cubature Kalman Filter (STRACKF) is proposed for the problem of satellite attitude estimation in the process of fast swing sweep. Several fading factors were introduced into the traditional Cubature Kalman filter algorithm and their positions were determined. Huber function was used to correct the measurement model with errors, so as to improve the tracking performance and adaptability of the filter algorithm for attitude mutation. The algorithm combines quaternion and modified Rodriguez parameters to facilitate attitude updating and avoid singularity. The proposed STRACKF algorithm is simulated, and compared with traditional UKF and CKF algorithms, the simulation shows that STRACKF algorithm has higher attitude estimation accuracy and the ability to deal with attitude mutation.