Time-interleaved analog-to-digital converters can significantly increase system sampling rate, but channel mismatches, such as gain mismatch, offset mismatch and phase mismatch, can cause great error in the system. The problem of broadband mismatches calibration for multichannel time-interleaved analog-to-digital converters system will be discussed in this paper by using the foreground calibration technique. Essentially, the calibration can be divided into two steps: First, all the mismatches between analog input and digital output will be measured by taking a linear frequency modulated signal as the reference signal; Next, a set of finite-impulse response filters will be calculated based on those mismatches to generate frequency dependent mismatches compensation, so that the signal to noise and distortion ratio, and spurious-free dynamic range will not be limited by the channel mismatches. There are two advantages of the proposed method when compared with previous methods. The complexity of error estimation can be reduced dramatically and high precision error estimation can also be achieved. In order to evaluate the performance, a series of experiments were conducted in a two-channel time-interleaved analog-to-digital converter system. The results show that the spurious-free dynamic range is improved by more than 10dB.

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%.

The escalating threat posed by low-altitude swarm targets underscores the critical need for precise tracking and situation awareness to secure key areas. While existing tracking methods based on random matrix theory offer promising opportunities, they face significant challenges. The high similarity among swarm targets, combined with radar resolution limitations, often leads to instabilities in target counts and measurements due to occlusion, environmental factors, and other disturbances, significantly increasing tracking complexity. To address these challenges, we design a digital staring radar system integrated with an adaptive random matrix method for efficient tracking of low-altitude swarm targets. The system achieves full spatiotemporal coverage without beam scanning or complex resource scheduling, enabling simultaneous detection and tracking of multiple targets. Algorithmically, the random matrix model is enhanced by introducing extension parameters to accurately capture the dynamic changes in swarm shape. Leveraging an adaptive Rao-Blackwellized Particle Filter (RBPF), the presented method jointly estimates the motion and extension states of swarm targets. Extensive simulation experiments and real-data validation demonstrate that the proposed method significantly improves the estimation accuracy for swarm extension states under complex shape variations while maintaining high precision in motion state estimation. This work provides a practical and effective solution for countering low-altitude swarm threats, with strong potential for real-world security applications.

Ubiquitous radar has significant advantages over traditional radar in detecting and identifying low, slow, and small (LSS) targets in a strong clutter environment. It effectively addresses challenges faced in low-altitude target monitoring within the low-altitude economy (LAE). The working mode of ubiquitous radar, which tracks first and identifies later, provides high-resolution Doppler data to the target identification module. Utilizing high-resolution Doppler data allows for the effective identification of LSS targets. To meet the needs of real-time classification, this paper first designs a real-time classification process based on sliding window Doppler data. This process requires the classifier to classify targets based on multiple rows of high-resolution Doppler spectra within the sliding window. Secondly, a multi-channel parallel perception network based on a 1D ResNet-SE network is designed. This network captures features within the rows of sliding window data and integrates inter-row features. Experiments show that the designed real-time classification process and multi-channel parallel perception network meet real-time classification requirements. Compared to the 1D CNN-MLP multi-channel network, the proposed 1D ResNet-MLP multi-channel network improves the classification accuracy from 98.71% to 99.34%. Integrating the 1D Squeeze-and-Excitation (SE) module to form the 1D ResNet-SE-MLP network further enhances accuracy to 99.58%, with drone target accuracy, the primary focus of the LAE, increasing from 97.19% to 99.44%.

To enhance the adaptive capabilities of radar, this work focuses on the problem of joint waveform adaptation and data-assisted jamming suppression in multiple target tracking scenarios with high-power sidelobe jamming. Utilizing deep reinforcement learning (DRL) methods, we develop agents capable of decision-making based on environmental perception information, thus enabling beamforming for jamming suppression and enhancing tracking performance. Jamming suppression faces challenges due to the computational complexity of beamforming and the large sample size of jamming. Furthermore, the multidimensionality of waveform parameters and the fluctuating states of targets pose additional challenges. To address these challenges, we employ DRL for jamming number perception, leverage convolutional neural networks to extract features, and incorporate the self-attention mechanism for jamming number decision-making. For waveform adaptation, we segment each parameter into a distinct decision space, with distinct agents making decisions using value-decomposition networks. These agents are augmented with gated recurrent units to manage diverse target states and maintain historical data. The focus on tracking robustness and accuracy can be tailored by designing the reward function weights. A separate training approach is developed to obtain effective joint policies. To avoid signal-to-noise ratio (SNR) constraints and enhance applicability to real radar systems, we develop an intelligent radar tracking simulation at the signal level and evaluate the performance of the proposed method. Simulation results demonstrate that the receiver agent can balance the perception of jamming numbers and real-time capabilities to assist in jamming suppression. Furthermore, compared to criterion-based methods and an entropy reward-based method, the transmitter agent can enhance tracking robustness and accuracy by adapting waveform parameters. Moreover, field experiments are conducted to verify that the proposed DRL method can improve the actual multi-target tracking performance through waveform adaptation.

Dual-functional radar-communication (DFRC) systems have gained attention for their advantages in spectrum sharing and hardware integration. Frequency-hopping (FH) MIMO radar waveforms enable communication via index modulation but introduce challenges such as increased range sidelobes, suboptimal communication performance, and incompatibility with conventional MIMO detection. Based on these considerations, we propose a new DFRC waveform design that combines frequency-hopping code selection (FHCS), binary frequency-shift keying (BFSK), and permutation selection (PS), termed FHCS-BFSK-PS. This design utilizes FH code vectors, frequency codes, and carrier-to-antenna mapping matrices as modulation parameters, embedding communication information across three dimensions: FH code index, frequency, and spatial phase. For communications, leveraging the weak coupling among these parameters, a simple and efficient demodulation algorithm based on the matched filtering criterion is proposed. For radar detection, we analyze the impact of fast-time domain FH on conventional MIMO detection and propose a modified detection method. This approach calculates and sums the log-likelihood ratios across multiple subpulses, thereby avoiding the incoherent accumulation of steering vectors in the subpulse domain. Finally, simulation results demonstrate the feasibility of the proposed scheme in target detection and highlight its superior performance in terms of symbol error rate (SER), data rate, and ambiguity function characteristics.