Meng Xia

In the field of Multiple Input Multiple Output(MIMO) radar waveform design, how to design an orthogonal waveform with high performance is one of the current hot topics. The Orthogonal Frequency Division Multiplexing - Linear Frequency Modulation(OFDM-LFM) waveform has been widely studied due to its excellent characteristics in many aspects. However, the spatial synthesized signal autocorrelation function of the traditional OFDM-LFM waveform design method has the disadvantage of high grating sidelobes. Based on a detailed analysis of the factors contributing to the high grating sidelobes in traditional OFDM-LFM waveform design methods, in order to meet the requirement that the waveform in the radar system has good properties of correlation, an innovative and effective algorithm for optimizing waveforms has been introduced in this paper. It uses the initial phase and frequency modulation bandwidth as optimization variables simultaneously, with the integrated sidelobe level (ISL) of the waveform as the optimization target. This algorithm aims to solve the waveform optimization problem of the spatial synthesized signals of the traditional frequency-orthogonal linear frequency modulation waveform design method. For the proposed objective function, the alternating direction method of multipliers integrates with the gradient descent algorithm is used to alternately optimize the objective function and the iteration factor is dynamically adjusted based on the gradient descent method. Multiple numerical simulations have verified that the proposed method is superior to the traditional waveform design method in terms of correlation performance while ensuring the orthogonality between signals.

This paper introduces a robust waveform design method aimed at reducing the impact of electromagnetic interference in radar systems, thereby enhancing target detection accuracy. We propose utilizing a high-order p-norm to characterize the peak sidelobe level (PSL) of the waveform. Additionally, the method incorporates spectral zero-trapping within known interfering frequency bands to mitigate interference effects. A unified optimization objective function is developed to ensure optimal correlation properties of waveforms for dual-use in radar and communication systems. By employing the AdamW algorithm for dynamic adjustment of the iteration factor, combined with a gradient descent search, this method refines both the autocorrelation of the waveform and its resilience to known disturbances. Experimental results demonstrate that our approach significantly improves autocorrelation performance over randomly generated initial waveforms. Moreover, the introduction of spectral zero-trapping notably enhances interference suppression in targeted frequency bands, thereby boosting overall signal performance. Our method effectively balances interference rejection with the minimization of sidelobe levels, offering a pragmatic waveform solution for complex radar environments.

The orthogonal frequency-division multiplexing (OFDM) mode with a linear frequency modulation (LFM) signal as the baseband waveform has been widely studied and applied in multiple-input multiple-output (MIMO) radar systems. However, its high sidelobe levels after pulse compression affect the target detection of radar systems. For this paper, theoretical analysis was performed, to investigate the causes of high sidelobe levels in OFDM-LFM waveforms, and a novel waveform optimization design method based on deep neural networks is proposed. This method utilizes the classic ResNeXt network to construct dual-channel neural networks, and a new loss function is employed to design the phase and bandwidth of the OFDM-LFM waveforms. Meanwhile, the optimization factor is exploited, to address the optimization problem of the peak sidelobe levels (PSLs) and integral sidelobe levels (ISLs). Our numerical results verified the correctness of the theoretical analysis and the effectiveness of the proposed method. The designed OFDM-LFM waveforms exhibited outstanding performance in pulse compression and improved the detection performance of the radar.

This study exploits high-order p-norm to uniformly represent the peak sidelobe level (PSL) and integrated sidelobe level (ISL) commonly used in waveform optimization, and presents a unified optimization objective function by considering the requirement that waveforms have good correlation properties in radar and communication systems. A novel and efficient waveform optimization algorithm is then proposed, which tackles the waveform and waveform set optimization of the PSL, the weighted sidelobe level, the ISL, and the weighted ISL. Applying the unified optimization objective function, the iteration factors are dynamically adjusted based on the quasi-Newton algorithm; in addition, the objective function is optimized with the gradient descent search algorithm and includes the optimization of autocorrelation and cross-correlation. Because certain application scenarios involve the Doppler sensitivity of waveforms, the problem is converted to cross-correlation optimization via channel division of Doppler frequency, and waveform with anti-Doppler sensitivity can be obtained. Multiple numerical simulations illustrate the positive performance of the proposed method. Furthermore, compared with certain traditional algorithms, the waveforms designed by this algorithm demonstrate improved correlation properties.

Automatic modulation recognition is a key technology in non-collaborative communication. However, it is affected by complex electromagnetic environments, leading to low recognition accuracy. To address this problem, this paper develops a ResNext signal recognition model based on an attention mechanism. Firstly, a channel, including additive Gaussian white noise (AWGN), Rician multipath fading, and clock offset, is created to simulate the complex electromagnetic environment, and transmission-impaired modulated signals with various signal-to-noise ratios (SNRs) are synthesized as a dataset. Secondly, using parallel stacked residual blocks of the same topology, instead of the residual blocks of ResNet, and introducing the attention layer (CBAM), the types of feature extraction are enriched without significantly increasing the parameter order of magnitude and avoiding the over-fitting phenomenon caused by depth deepening. The results show that the signal recognition method, based on the improved neural network framework, outperformed other deep learning methods, and the recognition rate obtained of 10 different modulation types of signals was above 90% at SNRs greater than 0 dB. The proposed signal recognition method achieved accurate recognition in complex electromagnetic environments.

Owing to advances in the overall performance and anti-interception capability of radars, the designs of radar waveforms with good correlation properties have been a concern for researchers. In this paper, we propose a novel method based on convolutional neural networks (CNNs) for designing single or multiple unimodular sequences with good auto- and cross-correlation or weighted correlation properties. The framework of the neural networks for sequence optimization is constructed using group convolution and identity mapping, and three different loss functions are presented using different optimization objectives. To illustrate the performance of the proposed method, we present numerous examples, including the design of sequences with low autocorrelation sidelobes in a specified lag interval and a sequence set with good auto- and cross-correlation properties. Moreover, an analysis of the simulations shows that the sequences designed through our method demonstrate better correlation properties than classic algorithms.