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The Solar System Boundary Exploration (SSBE) mission is the focal point for future far-reaching space exploration. Due to the SSBE having many scientific difficulties that need to be studied, such as a super long space exploratory distance, a super long flight time in orbit, and a significant communication data delay between the ground and the probe, the probe must have sufficient intelligence to realize intelligent autonomous navigation. Traditional navigation schemes have been unable to provide high-accuracy autonomous intelligent navigation for the probe independent of the ground. Therefore, high-accuracy intelligent astronomical integrated navigation would provide new methods and technologies for the navigation of the SSBE probe. The probe of the SSBE is disturbed by multiple sources of solar light pressure and a complex, unknown environment during its long cruise operation while in orbit. In order to ensure the high-accuracy position state and velocity state error estimation for the probe in the cruise phase, an autonomous intelligent integrated navigation scheme based on the X-ray pulsar/solar and target planetary Doppler velocity measurements is proposed. The reinforcement Q-learning method is introduced, and the reward mechanism is designed for trial-and-error tuning of state and observation noise error covariance parameters. The federated extended Kalman filter (FEKF) based on the Q-learning (QLFEKF) navigation algorithm is proposed to achieve high-accuracy state estimations of the autonomous intelligence navigation system for the SSBE probe cruise phase. The main advantage of the QLFEKF is that Q-learning combined with the conventional federated filtering method could optimize the state parameters in real-time and obtain high position and velocity state estimation (PVSE) accuracy. Compared with the conventional FEKF integrated navigation algorithm, the PVSE navigation accuracy of the federated filter integrated based the Q-learning navigation algorithm is improved by 55.84% and 37.04%, respectively, demonstrating the higher accuracy and greater capability of the raised autonomous intelligent integrated navigation algorithm. The simulation results show that the intelligent integrated navigation algorithm based on QLFEKF has higher navigation accuracy and is able to satisfy the demands of autonomous high accuracy for the SSBE cruise phase. [ABSTRACT FROM AUTHOR]

Ubiquitous Radar has become an essential tool for preventing bird strikes at airports, where accurate target classification is of paramount importance. The working mode of Ubiquitous Radar, which operates in track-then-identify (TTI) mode, provides both tracking information and Doppler information for the classification and recognition module. Moreover, the main features of the target's Doppler information are concentrated around the Doppler main spectrum. This study innovatively used tracking information to generate a feature enhancement layer that can indicate the area where the main spectrum is located and combines it with the RGB three-channel Doppler spectrogram to form an RGBA four-channel Doppler spectrogram. Compared with the RGB three-channel Doppler spectrogram, this method increases the classification accuracy for four types of targets (ships, birds, flapping birds, and bird flocks) from 93.13% to 97.13%, an improvement of 4%. On this basis, this study integrated the coordinate attention (CA) module into the building block of the 34-layer residual network (ResNet34), forming ResNet34_CA. This integration enables the network to focus more on the main spectrum information of the target, thereby further improving the classification accuracy from 97.13% to 97.22%. [ABSTRACT FROM AUTHOR]

Based on the characteristic of ubiquitous radar emitting low-gain wide beam, a method of long-time coherent integration (LTCI) is required to enhance target detection capability. However, high-speed and highly maneuverable targets can cause Doppler frequency migration (DFM), range migration (RM), and velocity ambiguity (VA), severely degrading the performance of LTCI. Additionally, the number of targets is unknown and variable, and the presence of clutter further complicates the target tracking problem. To address these challenges, we propose a hybrid integration method to achieve joint detection and estimation of multiple high-speed, and highly maneuverable targets. Firstly, we compensate for first-order RM using the keystone transform (KT) and generate corresponding sub-range-Doppler (SRD) planes with different folding factors to achieve VA compensation. These SRD planes are then stitched together to form an extended range-Doppler (ERD) plane, which covers a broader velocity range. Secondly, during the track-before-detect (TBD) process, tracking is performed directly on the ERD plane. We use the sequential Monte Carlo (SMC) approximation of the probability hypothesis density (PHD) to propagate multi-target states. Additionally, we propose an amplitude-based adaptive prior distribution method and a line spread model (LSM) observation model to compensate for DFM. Since the acceleration of the target is included in the particle state, using particles to search for DFM does not increase the computational load. To address the issue of misclassifying mirror targets as real targets in the SRD plane, we propose a particle space projection method. By stacking the SRD planes to create a folding range-Doppler (FRD) space, particles are projected along the folding factor dimension, and then, the particles are clustered to eliminate the influence of the mirror targets. Finally, through simulation experiments, the superiority of the LSM for targets with acceleration was demonstrated. In comparative experiments, the proposed method showed superior performance and robustness compared to traditional methods, achieving a balance between performance and computational efficiency. Furthermore, the proposed method's capability to detect and track multiple high-speed and highly maneuverable targets was validated using actual data from a ubiquitous radar system. [ABSTRACT FROM AUTHOR]