Lichao Yang

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.

In recent years, due to the emergence of ultrahigh resolution (UHR) radar systems, the bandwidth of radar has been increasing. However, conventional imaging algorithms have difficulties in the translational compensation of UHR inverse synthetic aperture radar (ISAR) data with high order range cell migration (RCM) and phase error. In this paper, a high-precision translational compensation method based on power flatness optimization (PFO) is proposed. The algorithm constructs the optimal criterion of power flatness and ensure the complete extraction of the prominent point energy. Then, the original data is compensated in the two-dimensional data domain through the prominent point phase inversion matrix. The results show that the proposed algorithm has significantly improved accuracy in the translational compensation of ultrahigh-resolution ISAR imaging compared with traditional algorithms.

The microwave photonic (MWP) radar technique is capable of providing ultrawide frequency bandwidth waveforms to generate ultrahigh-resolution (UHR) inverse synthetic aperture radar (ISAR) imagery. Nevertheless, conventional ISAR imaging algorithms have limitations in focusing UHR MWP-ISAR imagery, where high-precision high-order range cell migration (RCM) and phase correction are crucially necessary. In this article, a UHR MWP-ISAR imaging algorithm integrating rotation estimation and high-order motion terms compensation is proposed. By establishing the relationship between parametric ISAR rotation model and high-order motion terms, an average range profile sharpness maximization (ARPSM) is developed to obtain rotation velocity by using nonuniform fast Fourier transform (NUFFT). Second-order range-dependent RCM is corrected with parametric compensation model by using the rotation velocity estimation. Furthermore, the spatial-variant high-order phase error is extracted to compensation by the entire image sharpness maximization (EISM). A new imaging framework is established with two one-dimensional (1-D) parameter estimations: ARPSM and EISM. Extensive experiments demonstrate that the proposed algorithm outperforms traditional ISAR imaging strategies in high-order RCM correction and azimuth focusing performance.