Search Results (87)

A linear frequency modulation (LFM) signal is widely used in radar systems. However, its inherently high autocorrelation sidelobes can degrade weak-target detection, while amplitude and phase distortions caused by transmitter systems may further elevate sidelobe levels. To address these issues, a joint pre-compensation and windowing optimization framework is proposed for a transmitter-distorted LFM signal. First, a regularized pre-compensation filter with gain constraints is constructed to compensate for transmitter-induced distortions and restore the waveform. Considering that the system frequency response is difficult to estimate accurately in practice, amplitude and phase perturbations are introduced, and a pre-compensation filter under perturbation is derived to improve robustness. To overcome the limited flexibility of fixed windows, a parameterized cosine-series window is employed, and the firefly algorithm is employed to jointly optimize the window coefficients and width, achieving a better trade-off among peak sidelobe ratio, integral sidelobe ratio, main lobe width, and peak-to-average power ratio. Simulation results demonstrate that the proposed method compensates transmitter distortions, significantly suppresses autocorrelation sidelobes, and maintains favorable performance under perturbations. Full article

Ground-based scatterometers are widely used for quantitative microwave backscattering measurements in soil moisture retrieval, vegetation monitoring, and satellite scatterometer validation. However, low-cost software-defined radio (SDR) transceivers provide limited instantaneous bandwidth, making it difficult to transmit and process signals with bandwidths on the order of hundreds of MHz for fine range resolution, especially for systems requiring real-time onboard processing. To address this problem, this paper presents a vehicular, fully polarimetric, SDR-based scatterometer that achieves an equivalent wideband response by sequentially transmitting adjacent narrow subbands and coherently synthesizing them onboard. To enable real-time operation on a resource-limited field-programmable gate array/system-on-chip (FPGA/SoC) platform, we adopt a frequency-domain synthesis-pulse-compression pipeline that avoids interpolation and eliminates repeated matched filtering across subbands. A slot-based online phase calibration is performed within the settling window after each fast lock to estimate and compensate random local oscillator (LO) phase offsets, preserving coherent stitching. In addition, pulse repetition within each subband and coherent accumulation are integrated to improve the signal-to-noise ratio (SNR) under real-time throughput constraints. A Zynq-based implementation demonstrates deterministic onboard range-profile output, with a minimum processing latency of about 1.57 ms per frame. Loopback and outdoor experiments validate the equivalent 200 MHz bandwidth (five 40 MHz subbands), achieving approximately 0.75 m resolution and yielding sidelobe metrics consistent with the designed windowing, including a peak sidelobe ratio (PSLR) of −27.43 dB and an integrated sidelobe ratio (ISLR) of −12.38 dB. Field scans over farmland further show consistent ${\mathsf{\sigma }}_0$ trends across incidence angle and azimuth, indicating reliable onboard quantitative backscattering measurement. These results demonstrate that the proposed method provides a feasible solution for deterministic real-time equivalent wideband scatterometry on a low-cost SDR platform. Full article

Matched Field Processing (MFP) suffers from environmental mismatch in deep-sea long-range source localization. Although Frequency Difference Matched Field Processing (FDMFP) improves mismatch tolerance, it fails due to caustic phase effects. Frequency Difference Source Localization (FDSL) effectively compensates for caustic phase errors by applying frequency-difference processing to both the measured field and the replica field. However, conventional FDSL typically relies on large-aperture arrays with numerous elements, resulting in high deployment costs and bulky systems. Furthermore, it exhibits limited resolution and elevated sidelobes. These limitations are exacerbated under reduced element counts and low signal-to-noise ratio (SNR) conditions. To improve performance under low SNR and small-array configurations, this paper proposes the FDSL-MVDR and FDSL-MUSIC methods by deriving adaptive weight vectors based on the frequency-difference covariance structure and redefining the ambiguity surface. Numerical simulations in a deep-sea Munk environment (source range 195 km, depth 1000 m) using a 15-element vertical line array demonstrate that the adaptive FDSL methods outperform conventional FDSL in terms of peak sharpness and sidelobe suppression. FDSL-MUSIC achieves approximately 100% localization success at SNR = −5 dB, a 4 dB improvement over conventional FDSL. Performance analyses under representative environmental mismatches indicate that the adaptive FDSL methods maintain robust localization performance and high-resolution characteristics in complex deep-sea environments. These results validate the feasibility of high-precision deep-sea localization using a few-element array. Full article

To obtain radar images of a group of small space objects or to resolve individual elements of complex space objects in near-Earth orbit, a radar system must have high spatial resolution. High range resolution is achieved by using complex probing signals with a wide spectrum bandwidth. Achieving high angular resolution for small or complex space objects is based on the inverse synthetic aperture antenna effect. Among the various classes of complex signals, only two have found practical application in Inverse Synthetic Aperture Radar (ISAR) systems so far: the Linear Frequency-Modulated signal (chirp) and the Stepped-Frequency signal. Over the coherent integration interval of the echo signals, which corresponds to the ISAR aperture synthesis time, the combined correlation characteristics of the signal ensemble are analyzed. A high level of integral correlation noise in the ensemble of probing signals degrades the quality of the radar image. Therefore, a probing signal with a Zero Autocorrelation Zone (ZACZ) is highly relevant for ISAR applications. In this work, through simulation, radar images of a complex space object were obtained using both chirp and ZACZ probing signals. A comparative analysis of the correlation characteristics of the echo signals and the resulting radar images of the complex space object was performed. Full article

To enhance the accuracy and timeliness of field testing for grounding impulse impedance in complex soil environments, this paper addresses the limitations of traditional peak-ratio methods—such as susceptibility to noise interference and the inability to reflect dynamic impedance variations—by proposing an identification method that combines an improved Hanning window with recursive least squares (RLS). During signal preprocessing, an improved Hanning window with adjustable parameters and energy normalization is employed to enhance the main-lobe energy concentration of impulse voltage and current signals while effectively suppressing high-frequency sidelobe leakage. In the parameter estimation stage, a low-order discrete linear model is established and an RLS algorithm with a forgetting factor is introduced to achieve full-time adaptive estimation of impulse impedance. Using a simulated surge test circuit, 18 sets of typical operating conditions with varying inductance and resistance parameters are designed. The same voltage and current data are processed using three processing methods: no windowing, standard Hanning windowing, and improved Hanning windowing. Results show that the average relative error of surge impedance is 9.16% without windowing, the standard Hanning window reduced the error to 3.78%, and the modified Hanning window further decreased the error to approximately 1.51%. Comparative analysis of different forgetting factor settings indicates that a value of approximately λ = 0.98 achieves an optimal trade-off between dynamic tracking capability and steady-state smoothness. The research results demonstrate that the proposed method achieves high identification accuracy for impact impedance and exhibits satisfactory parameter robustness under strong noise and multiple operating conditions, providing a reference for grounding impact characteristic testing and lightning protection design. Full article

Background/Objectives: Single plane wave imaging (SPWI) offers ultrafast acquisition rates suitable for real-time ultrasound imaging applications; however, its image quality is compromised by beamforming artifacts such as sidelobe and grating lobe interferences. Methods: In this paper, we introduce an unsupervised beamforming framework based on adaptive deep coherence loss (DCL-A), which employs linear (${\alpha }_{\mathrm{linear}}$) or nonlinear weighting (${\alpha }_{\mathrm{nonlinear}}$) within the coherence loss function to enhance the artifact suppression and improve overall image quality. During training, the adaptive weight (α) is determined by the angular distance between the input and target PW frames, assigning lower α values for smaller distances and higher α values for larger distances. Therefore, this adaptability enables the method to surpass conventional DCL (no weighting) by emphasizing the different spatial correlation characteristics of mainlobe and sidelobe signals. To assess the performance of the proposed method, we trained and validated the network using publicly available datasets, including simulation, phantom and in vivo images. Results: In the simulation and phantom studies, the DCL-A with ${\alpha }_{\mathrm{nonlinear}}$ outperformed the comparison methods (i.e., conventional DCL and DCL-A with ${\alpha }_{\mathrm{linear}}$) in terms of peak range sidelobe level (PRSLL), achieving 7 dB and 14 dB greater sidelobe suppression, respectively, while maintaining a comparable full width at half maximum (FWHM). In the in vivo study, it achieved the highest contrast resolution among the comparison methods, yielding 2% and 3% improvements in generalized contrast-to-noise ratio (gCNR), respectively. Conclusions: These results demonstrate that the proposed deep learning-based beamforming framework can significantly enhance SPWI image quality without compromising frame rate, indicating promising potential for high-speed, high-resolution clinical applications such as cardiac assessment and real-time interventional guidance. Full article

Stepped-carrier orthogonal frequency division multiplexing (SC-OFDM) radar is an emerging low-cost alternative to standard OFDM radar for automotive applications due to providing high-range resolution at a low sampling rate. However, it is limited by inter-carrier interference (ICI) and velocity ambiguity in high-speed target detection. To address these issues, this paper proposes a two-step method for SC-OFDM radar. The method first applies multi-hypothesis Doppler compensation and leverages peak sidelobe ratio (PSLR) in the range profile as a distinguishing feature to estimate the target’s unambiguous velocity. Then, target signals are reconstructed into components free from ICI. Simulation results confirm the effectiveness of the proposed method. Compared to existing methods, this approach eliminates ICI without repeating OFDM symbols, thereby preserving communication data rate and enhancing suitability for integrated sensing and communication (ISAC) applications. Full article

This work proposes a photonics-aided W-band integrated sensing and communication (ISAC) system using photonics-aided frequency multiplication to suppress phase noise. Conventional dual-laser architectures suffer from phase noise accumulation, degrading both communication reliability and sensing resolution. To address this, we integrate photonics-aided frequency multiplication with orthogonal frequency-division multiplexing (OFDM), enabling a unified signal structure that simultaneously encodes communication data and radar waveforms without redundant resource allocation. Theoretical analysis reveals phase noise cancellation through coherent beating of symmetrically filtered sidebands in the photodetector (PD). Results demonstrate concurrent delivery of probability shaping (PS)-256QAM OFDM signals with a symbol error rate below 4.2 × 10⁻² and radar sensing with a 13.6 dB peak-to-sidelobe ratio (PSLR). Under a 1 MHz laser linewidth, the system achieves a 3.2 dB PSLR improvement over conventional methods, validating its potential for high-performance ISAC in beyond-5G networks. Full article

Integrated sensing and communication (ISAC) is a transformative technology for sixth-generation (6G) wireless networks. In this paper, we investigate end-to-end constellation mapping and demapping in ISAC systems, leveraging OFDM-based waveforms and an adaptive DNN architecture for pulse-based transmission. Specifically, we propose an end-to-end autoencoder framework that optimizes the constellation through adaptive symbol distribution shaping via deep learning, enhancing communication reliability with symbol mapping and boosting sensing capabilities with an improved peak-to-sidelobe ratio (PSLR). The autoencoder consists of an autoencoder mapper (AE-Mapper) and an autoencoder demapper (AE-Demapper), jointly trained using a composite loss function to optimize constellation points and achieve flexible performance balance in communication and sensing. Simulation results demonstrate that the proposed DNN-based end-to-end design achieves dynamic balance between PSLR of the autocorrelation function (ACF) and bit error rate (BER). Full article

High-resolution three-dimensional (3D) Inverse Synthetic Aperture Radar (ISAR) imaging is essential for the characterization of target scattering in various environments. The practical application of this technique is frequently impeded by the lengthy measurement time necessary for comprehensive data acquisition with turntable-based systems. Sub-sampling the aperture can decrease acquisition time; however, traditional reconstruction algorithms that utilize matched filtering exhibit significantly impaired imaging performance, often characterized by a high peak side-lobe ratio. A methodology is proposed that integrates compressed sensing(CS) theory with sparse-aperture optimization to achieve high-fidelity 3D imaging from sparsely sampled data. An optimized sparse sampling aperture is introduced to systematically balance the engineering requirement for efficient, continuous turntable motion with the low mutual coherence desired for the CS matrix. A deep Bayesian optimization framework was developed to automatically identify physically realizable optimal sampling trajectories, ensuring that the sensing matrix retains the necessary properties for accurate signal recovery. This method effectively addresses the high-sidelobe problem associated with traditional sparse techniques, significantly decreasing measurement duration while maintaining image quality. Quantitative experimental results indicate the method’s efficacy: the optimized sparse aperture decreases the number of angular sampling points by roughly 84% compared to a full acquisition, while reconstructing images with a high correlation coefficient of 0.98 to the fully sampled reference. The methodology provides an effective solution for rapid, high-performance 3D ISAR imaging, achieving an optimal balance between data acquisition efficiency and reconstruction fidelity. Full article

Maritime search and rescue (SAR) demands reliable sensing and communication under sea clutter. Emerging integrated sensing and communication (ISAC) technology provides new opportunities for the development and modernization of maritime radio communication, particularly in relation to search and rescue. This study investigated the dual-function capability of a phase-coded frequency modulated continuous wave (FMCW) system for search and rescue at sea, in particular for life signs detection in the presence of sea clutter. The detection capability of the FMCW system was enhanced by applying phase-modulated codes on chirps, and radar-centric communication function is supported simultaneously. Various phase-coding schemes including Barker, Frank, Zadoff-Chu (ZC), and Costas were assessed by adopting the peak sidelobe level and integrated sidelobe level of the ambiguity function of the established signals. The interplay of sea waves was represented by a compound K-distribution model. A multiple-input multiple-output (MIMO) architecture with the ZC code was adopted to detect multiple objects with a high resolution for micro-Doppler determination by taking advantage of spatial coherence with beamforming. The effectiveness of the proposed method was validated on the 4-transmit, 4-receive (4 × 4) MIMO system with ZC coded FMCW signals. Monte Carlo simulations were carried out incorporating different combinations of targets and user configurations with a wide range of signal-to-noise ratio (SNR) settings. Extensive simulations demonstrated that the mean squared error (MSE) of range estimation remained low across the evaluated SNR setting, while communication performance was comparable to that of a baseline orthogonal frequency-division multiplexing (OFDM)-based system. The high performance demonstrated by the proposed method makes it a suitable maritime search and rescue solution, in particular for vision-restricted situations. Full article

Ultrasonic testing (UT) is a vital nondestructive testing (NDT) technique used to evaluate the integrity of materials and structures. However, conventional excitation signals often suffer from significant attenuation in highly attenuative materials, resulting in low signal energy and poor signal interpretation. Coded excitation techniques, such as the Barker code and the complementary Golay code (CGC), have been used to enhance signal energy and signal-to-noise ratio. Yet, Barker codes are limited by short sequence lengths, while CGC requires two transmission events, reducing time efficiency. This paper proposes a novel excitation method: the Barker-convolved mutually orthogonal Golay complementary code (BMOGCC). By convolving the Barker code with the mutually orthogonal Golay complementary code (MOGCC), BMOGCC combines the advantages of both, including flexibility in code length, improved signal amplitude, low sidelobe levels, and enhanced time efficiency. Performance was evaluated using numerical simulations and laboratory experiments, with key indices including the peak sidelobe level (PSL), mainlobe gain (MG), and temporal resolution. The results show that BMOGCC achieves a significantly higher MG than either the Barker code or MOGCC alone while maintaining a low PSL and preserving the temporal resolution. These findings demonstrate that BMOGCC is effective and efficient for coding excitation signals in ultrasonic testing, offering improved signal quality and measurement time efficiency. Full article

In this paper, we propose a compressionmethod for optical encryption using singular value decomposition (SVD). Double random phase encryption (DRPE), which employs two distinct random phase masks, is adopted as the optical encryption technique. Since the encrypted data in DRPE have the same size as the input data and consists of complex values, a compression technique is required to improve data efficiency. To address this issue, we introduce SVD as a compression method. SVD decomposes any matrix into simpler components, such as a unitary matrix, a rectangular diagonal matrix, and a complex unitary matrix. By leveraging this property, the encrypted data generated by DRPE can be effectively compressed. However, this compression may lead to some loss of information in the decrypted data. To mitigate this loss, we employ volumetric computational reconstruction based on integral imaging. As a result, the proposed method enhances the visual quality, compression ratio, and security of DRPE simultaneously. To validate the effectiveness of the proposed method, we conduct both computer simulations and optical experiments. The performance is evaluated quantitatively using peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and peak sidelobe ratio (PSR) as evaluation metrics. Full article

Three-dimensional (3D) radar imaging is essential for target detection and measurement of scattering characteristics. Cylindrical scanning, a prevalent spatial sampling technique, provides benefits in engineering applications and has been extensively utilized for assessing the radar stealth capabilities of large aircraft. Traditional cylindrical scanning generally utilizes highly sampled full-coverage techniques, leading to an excessive quantity of sampling points and diminished image efficiency, constraining its use for quick detection applications. This work presents an efficient 3D sampling strategy that integrates vertical linear scanning with horizontal rotating motion to overcome these restrictions. A joint angle–space sampling model is developed, and geometric constraints are implemented to enhance the scanning trajectory. The experimental results demonstrate that, compared to conventional techniques, the proposed method achieves a 94% reduction in the scanning duration while maintaining a peak sidelobe level ratio (PSLR) of 12 dB. Furthermore, this study demonstrates that 3D imaging may be accomplished solely by a “V”-shaped trajectory, efficiently determining the minimal possible sampling aperture. This approach offers novel insights and theoretical backing for the advancement of high-efficiency, low-redundancy 3D radar imaging systems. Full article

In recent years, several advantages of noise radars have positioned this technology as a promising alternative to conventional radar technology. Immunity to jamming, low mutual interference, and low probability of interception are good examples of these advantages. However, the nature of random sequences introduces several issues, such as fluctuations in the range sidelobes of the autocorrelation function causing high sidelobe levels, hence not exploitable by radar systems. This study introduces the use of multi-objective evolutionary (MOE) algorithms to design noise radar waveforms with good autocorrelation properties as well as a low peak-to-average power ratio (PAPR). A set of Pareto-optimal waveforms are produced and, most importantly, entropy is introduced as a constraint in order to maintain the transmitted signal close to a full non-deterministic waveform. Moreover, a relation between PAPR and negentropy (negative entropy) is established theoretically and validated with other authors’ simulations. Full article