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This paper focuses on the design of a frequency modulated continuous wave (FMCW) dual-functional radar-communication (DFRC) system with low-loss radar detection performance, aiming at achieving a balance among communication rate, symbol error rate (SER), and out-of-band leakage. For what the communication functionality concerns, the constrained frequency hopping (C–FH) sequence mapping and up/down chirp are used to embed data, limiting the out-of-band leakage. We provide explicit rules for traversal-FH sequence to adjust the number of configurable frequency points within each chip, and limit the SER at the receiver. For the radar signal processing, after coherent processing and sampling, we proceed time-domain extension and range-bin phase compensation (TE-RBPC) according to the FH value, achieving the same range profile as an unmodulated (FMCW) without any influence from the FH sequences. The simulation results show that the proposed C–FH-chirp waveform reduces the out-of-band power leakage by 10 dB if compared with a phase shift keying scheme, and provides a 5 dB improvement in the signal noise ratio under the same SER if compared with an unconstrained FH-chirp scheme. Moreover, by comparing it with a scheme without compensation, the sidelobe amplitude of radar range profile in TE-RBPC scheme is reduced by about 39 dB. [ABSTRACT FROM AUTHOR]

In this paper, we propose a pulse train coding and decoding matrix design based electronic counter-countermeasure (ECCM) scheme for multiple-input multiple-output (MIMO) radar against interrupted sampling repeater jamming (ISRJ). The ECCM scheme includes two steps: jamming pulse compression (PC) sidelobes suppression and jamming peaks identification. By joint design of coding and decoding matrix, the proposed ECCM scheme can simultaneously suppress PC sidelobes of target echoes and jamming signals. Compared with the existing methods, the proposed scheme can achieve the improvement in jamming suppression and target detection without signal-to-noise ratio loss, and the coding and decoding matrix design can be realized offline. Besides, the phase-only coding matrix can be directly applied to the MIMO radar with multi-beam transmit beampattern. After sidelobes suppression, an identification matrix is proposed to identify the jamming peaks. In specifical situation, the identification process does not require ISRJ parameter, and the ISRJ parameter estimation can be converted into a convex optimization problem in other situations. At last, simulated results corroborate the effectiveness of the anti-ISRJ performance with the proposed ECCM scheme. • An overall ECCM scheme is proposed, including ISRJ sidelobes suppression, ISRJ parameter estimation and ISRJ peaks identification. • The proposed ECCM scheme can be directly applied to the MIMO radar with multi-beam. • In a particular case, the proposed ECCM scheme can perform without ISRJ parameters. • The proposed ECCM scheme does not suffer SNR loss. [ABSTRACT FROM AUTHOR]

• The article proposes a learnable method named TEFISTA-Net for high-resolution range profile reconstruction (HRRP) with low-frequency ultra-wideband radar, which is an unrolling network with target enhancement capability. • The method achieves lower computational complexity and reduced difficulties in algorithm parameter tuning, compared with existing competitors based on compressed sensing. • The proposed network structure can effectively reduce background artifacts and improve the robustness of HRRP reconstruction. • Simulated and measured radar data demonstrate the performance advantages of the proposed method, in terms of the reconstruction accuracy and target saliency of HRRP. In low-frequency ultra-wideband (LFW) radar, the reconstruction of high-resolution range profile (HRRP) is an important task. The state-of-the-art compressive sensing (CS) method for HRRP reconstruction based on geometrical theory of diffraction (GTD) requires manual algorithm parameter tuning and is computationally expensive. In this paper, we propose a deep learning-based CS method for LFW radar HRRP reconstruction. We design a neural network architecture, i.e., target enhancement-based FISTA-Net (TEFISTA-Net), by unrolling the fast iterative shrinkage thresholding algorithm (FISTA). A new loss function based on the target-to-background ratio (TBR) is introduced for network training with the target enhancement capability in low signal-to-noise ratio (SNR) scenarios. The algorithm parameters in the traditional CS methods are substituted by network parameters learned from training data, getting rid of manual parameter tuning. In addition, simple convolution operations in our new method lead to lower computational complexity compared with existing methods. Experimental results based on diverse data show that the proposed method leads to higher computational efficiency with similar or better performance in comparison with existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this paper, we deal with the point-like target fusion detection in a partially homogeneous environment with distributed MIMO radar. Specifically, we consider the imperfect waveform separation problem, which means that the matched filter output contains not only the auto-correlation term of the current matched waveform but also the cross-correlation terms, called waveform residuals, with the remaining transmitted waveforms. To this end, a hybrid-order Gaussian (HOG) model is utilized, where the target amplitude is deterministic but unknown, and the waveform residuals obey the Gaussian distribution. Then two adaptive detectors are developed according to the GLRT and Wald test, named HOG-GLRT and HOG-Wald respectively. At the fusion detection stage, we focus on the problem of signal-to-noise ratio (SNR) diversity, i.e., the detection performance degradation due to the average weighting of spatial diversity channels when the echo SNR differs. Combined with the Model Order Selection criterion and multiple hypothesis test, two modified fusion detectors based on channel selection are proposed, named MHOG-GLRT and MHOG-Wald. Finally, the numerical simulation results show that the HOG-GLRT is sensitive against waveform residuals, while the HOG-Wald exhibits strong robustness. And it also demonstrates the effectiveness of the MHOG-GLRT and MHOG-Wald facing the extreme scene of SNR diversity. • We propose two detectors for distributed MIMO radar under imperfect waveform separation. • We develop two modified detectors to solve the problem of SNR diversity. • These detectors can improve the detection performance compared with the existing ones. [ABSTRACT FROM AUTHOR]

Retransmission interference in radar and communication signal processing significantly threatens the received linear frequency modulation signal. With the advancement in the hardware capabilities of interference devices, they can intercept and retransmit signals within the duration of a single pulse. Therefore, we refer to this type of interference as intra-pulse retransmission interference, which includes retransmission modes smeared spectrum, interrupted sampling repeater jamming, and interrupted nonuniform sampling repeater jamming. Although traditional interference cancellation techniques offer optimal solutions under the assumption of fully known interference parameters, practical scenarios often involve unknown parameters, estimation errors, random parameters, and multiple interferences, leading to performance degradation. To address these problems, we propose a Multi-Residual Encoder-Decoder Network (MRDENet), a novel approach for effectively mitigating intra-pulse retransmission interferences and recovering the signal of linear frequency modulation. In contrast to parameter estimation and interference cancellation, MREDNet extracts crucial features of both the target and interference and performs reconstruction. More specifically, MREDNet acquires information about the retransmission pattern of interference and the modulation frequency through its multi-level residual shortcut connections. To address the issue of network parameters being real while the signals are complex, we devised decomplexification—a data preprocessing approach enabling neural networks for direct utilization in front-end complex signal processing. Besides, we evaluate the performance of MREDNet comprehensively. The experimental conditions encompassed single and multiple interferences, incomplete data sets, varying signal-to-noise ratios, and jamming-to-signal ratios. The computational simulations demonstrate that MREDNet successfully solves the problem of recovering linear frequency modulation signals interfered with by intra-pulse retransmission interference. According to the boundary condition of MREDNet, the proposed method exhibits valuable applications in real-world scenarios. • We propose a recovery network named MREDNet, which is employed for recovering LFM signals interfered by intra-pulse retransmission interference. • In contrast to other methods, MREDNet possesses the capability to batch-process multiple pulses and handle multiple interferences. • MREDNet is applicable in scenarios with varying interference types between the test and training data sets. • Simulations demonstrate that MREDNet still performs well even under conditions of high signal-to-interference ratios and low signal-to-noise ratios. [ABSTRACT FROM AUTHOR]

To improve the detectability of extended targets, a novel mismatched filter is designed, which deliberately broadens the 3 dB mainlobe width (MW) of the output response. Similar to how narrowband radar accumulates target energy, this filter concentrates the energy of scattering centers into the same resolution unit. To achieve this, constraints on 3 dB MW, mismatched filter gain, and signal-to-noise ratio (SNR) are imposed on the mismatched filter, while the integration sidelobe level (ISL) is minimized as the figure of merit. To handle the resulting non-convex problem, a polynomial-time method based on sequential convex approximation (SCA) which slacks the non-convex constraint is proposed to decrease ISL. Numerical results demonstrate successful broadening of the 3 dB MW within a certain range, and efficient accumulation of extended target energy. [ABSTRACT FROM AUTHOR]