The radial acceleration between the target and the observation platform causes Doppler frequency changes. High-precision estimation of Doppler change rate has broad application in the field of signal processing. To improve the processing gain,firstly, frequency domain accumulation is used within the pulse, the data for long time is diluted into limited spectral line data, then,the estimation of doppler rate of change is transformed into a least squares estimation through spectral correlation processing of interpulse. It is possible for inter pulse accumulation. The processing gain can be greatly improved, and the high estimation accuracy can be easily achieved by intra-pulse accumulation and multi-pulse least squares estimation. The difficulty of accurately estimating the frequency, time of arrival and initial phase is avoided by cleverly utilizing spectral correlation processing. This algorithm is insensitive to intra-pulse modulation and pulse repetition interval, and has strong universality. Finally, the validity, universality and high-precision estimation of the algorithm are verified through simulation in various scenarios.

A novel channel estimation algorithm based on energy ratio confidence with dynamic dual-threshold decision is proposed to address the issues of missed path detection and performance degradation in existing channel estimation algorithms for dual-antenna space-time block coding (STBC) communication systems within unmanned aerial vehicle (UAV) swarm networking scenarios. These issues arise due to fixed thresholds and insufficient noise handling within the channel response length. Significantly, the proposed algorithm does not require prior knowledge of the exact channel impulse response length. Simulation results demonstrate that the proposed algorithm achieves a performance gain of nearly 2 dB at a bit error rate (BER) on the order of 1E-5 compared to the conventional threshold-based Fourier Transform-Least Squares algorithm. Furthermore, the algorithm effectively suppresses the noise in the estimated channel impulse response, leading to significantly enhanced channel estimation performance for UAV swarm networking.

A data compression collaborative algorithm that integrates transmission optimization and delay control was proposed to address the data dimension explosion and complexity increase caused by the large-scale grid connection of distributed photovoltaic systems. Firstly, based on the theory of decision boundary sensitivity, an optimization framework for minimizing total delay was constructed, and an enhanced inter-class boundary preservation algorithm (EIPB) was proposed to reduce the amount of transmitted data by dynamically maintaining key instances of decision boundaries. Secondly, the traditional distance instance selection method was improved by proposing an enhanced instance selection algorithm (EIS) based on K-dimensional tree (KD-Tree) spatial partitioning, which utilized nearest neighbor search acceleration technology to enhance instance selection efficiency. Then, a dynamic error allocation sector lossless compression algorithm (DEASC) was proposed, which achieved collaborative optimization of compression efficiency and fidelity through adaptive slope constraints and multi-stage entropy encoding. Experimental verification showed that the EIPB-EIS joint algorithm improved the average compression ratio to 7.8 compared to traditional methods, reduced the root mean square error of reconstruction percentage to 0.51%, and reduced transmission delay by 62.7%, effectively solving the problem of efficient transmission and accurate reconstruction of high-dimensional photovoltaic data.

With the swift advancement of radar jamming techniques, the variety of active jamming types and the diversity of jamming strategies have surged, urging for accurate identification of jamming types. Conventional active jamming identification methods lack efficiency and universality. Meanwhile, current deep learning-based approaches are encumbered by large-scale parameters and the need for extensive data, which significantly limit their practical applications. To enhance recognition capabilities under conditions with limited parameters and data, a lightweight few-shot radar active jamming identification method based on multi-modality fusion is proposed. Lightweight fusion is achieved by leveraging the temporal locality of time-frequency features and the high-resolution range profile features. Additionally, few-shot classification performance is improved through exploiting metric learning and feature retrieval techniques. Experiments conducted on both simulated and measured datasets demonstrate the superior performance of the proposed method under a variety of conditions.

With the development of marine resources and the increasing demand for safety monitoring, the traditional underwater acoustic detection equipment has problems such as large size and difficult deployment. In order to meet the need of high precision underwater acoustic detection, a design method of seismic wave sensor based on micro-electro-mechanical system (MEMS)technology is proposed in this paper. By constructing the second-order dynamic model of the sensitive structure, the fully integrated closed-loop system control is realized by combining the ΣΔ modulator, and the behavior level modeling and non-ideal characteristic simulation optimization are completed by Simulink platform, and the chip integration is realized by 0.5 μm CMOS process. A fully differential precharge amplifier and a correlation double sampling circuit are designed. The test results show that the sensor sensitivity is up to 1.5V/g, the operating bandwidth is 300 Hz, the dynamic range is about 136 dB at 1 Hz bandwidth, the equivalent input noise is as low as 154 ng/√Hz, the nonlinearity is 0.065%, and the bias stability is about 28.2 μg, The FOM value is 204.5 μW·μg/Hz. The high-precision application potential of MEMS technology in the field of underwater acoustic detection is verified, and a miniaturized solution for seismic monitoring and marine environment perception is provided.

The problem of output voltage waveform oscillation in the 28 V radiation-resistant P-channel field effect transistor at a resistive load of 10.4 Ω was studied, and it was found that the main reason was the mismatch between the device's own parasitic parameters and the peripheral circuit parameters. To avoid this phenomenon, a circuit improvement solution was proposed, that is, the series resistance in the gate electrode reduces the V_GS change rate and destroys the conditions for oscillation. At the same time, the clamp diode is added to the gate source electrode to ensure that the gate source voltage V_GS is always lower than the rated value, so as to improve the safety of the circuit operation and long-term working reliability.

Existing deep learning-based modulation recognition methods are difficult to adapt to HF channels with significant time-varying characteristics, limiting their application in modulation recognition for High Frequency (HF) signals. In addition, in non-cooperative HF communication scenarios, the training dataset is often difficult to cover all possible modulation types, so that open-set modulation recognition also has important practical significance. This paper proposes a multi-modal feature fusion-based open-set modulation recognition method by combining communication domain knowledge and open-set recognition techniques,which effectively reduces the impact of time-varying HF channels and unknown modulation types on recognition performance. The proposed method first utilizes communication domain knowledge to obtain multimodal features that are robust to channel variations,and then extracts discriminative deep feature representations through multimodal feature fusion and deep feature learning to effectively identify known and unknown modulation types. In addition, the method also generates dummy samples through manifold mixing strategy to assist network training, which can enhance the network's ability to identify unknown types. Experimental results indicate that the proposed method outperforms existing open-set modulation recognition methods. When the channel conditions of training and testing signals are the same, the proposed method improves by over 3% in open-set recognition performance. When the channel condition of testing signals is drastically changed, that is, the channel conditions of the training and testing signals are different,the proposed method improves by over 8% compared to existing method, which exhibits strong robustness to channel variations.

In the field of aerospace tracking, telemetry, and command (TT&C) communications, the capability for precise telemetry, telecommand, and data transmission is a critical technology ensuring reliable spacecraft operations. As the complexity of space missions continues to escalate and the TT&C communication environment becomes increasingly demanding, higher requirements are imposed on the reliability of communication links in TT&C systems. Polar codes, as a short-frame burst coding scheme with high reliability, low complexity, and superior coding gain, yet exhibit high sensitivity to carrier frequency offset errors. Addressing the dual high demands for reliability and synchronization accuracy in TT&C systems, this paper proposes a Polar code-aided frequency offset estimation (PCAFOE) algorithm. Compared with the traditional carrier synchronization algorithm, PCAFOE algorithm is demonstrated with higher estimation accuracy, which is able to effectively improve the carrier synchronization performance of TT&C communication systems, and provides effective technical support for next-generation aerospace TT&C systems.

At present, GNSS-IR sea surface height retrieval technology relies primarily on traditional geodetic GNSS receivers. However, due to their high cost and portability limitations, these devices struggle to meet the demands for low-cost and high-precision applications, which restricts the widespread adoption of this technology. To address this, our study utilizes smartphones for GNSS-IR sea surface height inversion experiments. By employing robust estimation techniques, we integrated multi-system and multi-frequency data to enhance the accuracy of the sea surface height inversion. The results indicate that the smartphone's overall retrieval accuracy is approximately 21 cm, with an average correlation coefficient of 0.96, performance comparable to that of traditional GNSS receivers. The multi-system multi-frequency data fusion further improved the sea surface height retrieval accuracy to 6.8 cm, with a correlation coefficient of 0.996. Compared to the initial retrieval results, this approach significantly enhanced both the temporal resolution and the accuracy of sea surface height retrieval. After smoothing, the accuracy of the fused retrieval results reached 6.1 cm. These findings demonstrate that smartphones can achieve centimeterlevel accuracy in sea surface height retrieval, making them a viable alternative to traditional GNSS receivers for GNSS-IR applications.

Inertial/Global Navigation Satellite System (GNSS) integrated navigation has been widely applied in various mobile platforms such as unmanned aerial vehicles (UAVs). However, during GNSS signal outages, INS errors accumulate rapidly, severely degrading navigation accuracy. Existing research primarily focuses on horizontal two-dimensional error modeling while neglecting the dynamic characteristics in the vertical (altitude) direction, limiting its practical application in three-dimensional space. To address this issue, this paper proposes a dual-branch neural network model for three-dimensional navigation, which simultaneously models position increments in the longitude, latitude, and altitude directions to cater to the demands of dynamic navigation in 3D space. The model adopts a decoupled dual-branch structure built with LSTM and GRU networks, designing separate modeling paths for the horizontal and vertical components. A convolutional neural network (CNN) is further incorporated into the main branch to enhance temporal feature extraction. Experimental results demonstrate that the proposed network significantly improves three-dimensional navigation accuracy. Compared with conventional positioning methods, it reduces the root mean square error(RMSE) along the east, north, and up axes by 97.8 %, 97.9 %, and 26.2 %, respectively, demonstrating its strong potential for practical deployment.