The beat signal obtained from frequency-modulated continuous-wave (FMCW) radar is a waveform that is corrupted by noise and requires filtering out interference components for frequency calibration. Traditional FFT methods are affected by the fence effect and spectral leakage, leading to a reduction in frequency estimation accuracy. Therefore, an improved double-spectrum-line interpolation frequency estimation algorithm is proposed in this paper, utilizing spectral refinement and phase interpolation. Firstly, the post-FFT spectral signal is refined to narrow the frequency search range and enhance frequency resolution, thereby separating the noise signal. Then, a frequency deviation factor is defined based on the relationship between adjacent phase angles. Finally, the signal’s phase angles are interpolated using the frequency deviation factor to estimate the frequency of the beat signal. Experimental results demonstrate that the proposed algorithm reduces the impact of quantization on the frequency distribution and increases the signal’s noise resistance. The proposed algorithm has a higher accuracy and lower standard deviation compared to the recently proposed algorithm.

Frequency estimation of a sinusoidal signal is a fundamental problem in signal processing for the Internet of Things. The frequency interpolation estimation algorithm based on the fast Fourier transform is susceptible to being disturbed by noise, which leads to estimation error. In order to improve the accuracy of frequency estimation, an improved Rife frequency estimation algorithm based on phase angle interpolation is proposed in this paper, namely the PAI–Rife algorithm. We changed the existing frequency deviation factor of the Rife algorithm using phase angle interpolation. Then, by setting the frequency shift threshold, the frequency that is not within the threshold range is shifted to the optimal estimation space. The simulation results show that the proposed algorithm has a wider valid estimation range, and the estimated standard deviation is closer to the Cramer–Rao lower bound. Compared with the Rife algorithm and some recently proposed advanced algorithms, the proposed algorithm has less computational complexity, lower misjudgment rate, and more stable performance.

As the space environment becomes more complex, efficient detection and tracking of resident space objects (RSOs) in Earth's orbit is increasingly important. However, current single-satellite detection systems are limited by observation range and computational power, making real-time RSO awareness difficult. To address this challenge, we propose a collaborative detection model with reconfigurable satellites that leverages a federated learning framework to enable situational awareness and object detection across multiple nodes. Specifically, we designed a Modular Self-Reconfigurable Satellite consisting of four modules, with each module equipped with two cameras. By stitching together their fields of view, the constellation offers comprehensive situational awareness of the surrounding 2π annular space. The approach overcomes the limitation of single-point computing power by aggregating and updating network parameters among reconfigurable satellites, effectively addressing the risks associated with multi-modules data transmission. We conduct extensive experiments to validate the effectiveness of our proposed reconfigurable satellite in detecting RSOs. Experimental results show that the optical system constructed in this paper can effectively perceive 6th magnitude stars with an exposure time of 1 second, and the proposed detection model exhibits a maximum increase in accuracy of 5.2% compared with the single satellite model.

Wearable device-based human activity recognition (HAR) has attracted considerable interest with the rapid development of the Internet of Things (IoT), and federated learning (FL) has been widely adopted in this domain for its ability to collaboratively train models across decentralized devices while preserving privacy. However, its performance is hindered by data heterogeneity arising from variations in the placement of the wearable devices, user behaviors, and physiological characteristics. In this work, we present local HAR data alignment (LDA)-FedHAR, a federated HAR framework designed for wearable devices by capturing more common knowledge from aligned client HAR data. It performs LDA on each client, which is an entirely on-device alignment method that operates independently on local HAR data. By computing the transformation matrix solely from local HAR data and applying it to the data itself, LDA projects heterogeneous client data into a unified space, thereby reducing interclient discrepancies at the source. To further enhance efficiency and robustness, we propose two inertial measurement unit (IMU)-specific variants, LDA(S-IMU) and LDA(C-IMU), which explore intra- and inter-IMU correlations based on practical placements of wearable devices. Experiments are conducted on 4 public HAR datasets: heterogeneity HAR (HHAR), Shoaib2014, OPPORTUNITY++, and PAMAP2. The results show that LDA effectively reduces interclient discrepancies, and LDA-FedHAR along with its variants consistently outperforms state-of-the-art FL methods. Moreover, the improvements achieved by integrating LDA into other FL methods highlight its applicability.