Search Results (68)

Although the elevated orbit and highly squinted observation geometry bring advantages for medium-earth-orbit (MEO) synthetic aperture radar (SAR) in applications, they also complicate signal processing. The severe spatial variability of Doppler parameters and large extended range distribution of echo make it challenging for the traditional imaging algorithms to get the expected results. To quantify the variation, a spatially variable two-dimensional (SV2D) spectrum is established in this paper. The sufficient order and spatially variable terms allow it to preserve the features of targets both in the scene center and at the edge. In addition, the huge data volume and incomplete azimuth signals of edge targets, caused by the large range walk when MEO SAR operates in squinted mode, are alleviated by the variable pulse repetition interval (VPRI) technique. Based on this, a novel data-focusing method for highly squinted MEO SAR is proposed. The azimuth resampling, achieved through the non-uniform fast Fourier transform (NUFFT), eliminates the impact of most Doppler parameter space variation. Then, a novel imaging kernel is applied to accomplish target focusing. The spatially variable range cell migration (RCM) is corrected by a similar idea, with Doppler parameter equalization, and an accurate high-order phase filter derived from the SV2D spectrum guarantees that the targets located in the center range gate and the center Doppler time are well focused. For other targets, inspired by the non-linear chirp scaling algorithm (NCSA), the residual spatially variable mismatch is eliminated by a cubic phase filter during the scaling process to achieve sufficient focusing depth. The simulation results are given at the end of this paper and these validate the effectiveness of the method. Full article

The direction-of-arrival (DOA) estimation of multiple linear frequency modulation (LFM) signals typically requires the construction of a spatial time–frequency distribution (STFD) matrix via linear transforms or quadratic time–frequency distributions (QTFD) before joint spatial time–frequency estimation. Extensive research has been conducted on DOA estimation of LFM signals with overlapped instantaneous frequency (IF) trajectories and significantly different chirp rates. However, when LFM signals have the same chirp rate and slightly different initial frequencies with parallel and close IF trajectories, their linear transforms suffer from low resolution and quadratic distributions and are affected by cross-terms, both of which reduce accuracy. To address this problem, this study proposes a DOA estimation algorithm based on QTFD and the Hough transform. First, QTFD is used to improve the resolution and apply both spatial and directional smoothing to eliminate cross-terms. Second, the Hough transform is employed for IF estimation instead of threshold filtering to enhance accuracy. Finally, DOA results are obtained via time–frequency filtering and the multiple signal classification (MUSIC) algorithm. Experiments show that for two LFM signals at a −5 dB signal-to-noise ratio (SNR), the proposed algorithm improves accuracy by approximately 43.2% compared to similar algorithms and effectively estimates the DOA in underdetermined cases. Thus, the proposed algorithm enhances the DOA estimation accuracy for multiple LFM signals, is robust to noise, and expands the application scenarios of joint spatial time–frequency estimation. Full article

Light detection and ranging (LiDAR) sensors are essential for applications where high-resolution distance and velocity measurements are required. In particular, frequency-modulated continuous wave (FMCW) LiDAR, compared with other LiDAR implementations, provides superior receiver sensitivity, enhanced range resolution, and the capability to measure velocity. Integrating LiDARs into electronic and photonic semiconductor chips can lower their cost, size, and power consumption, making them affordable for cost-sensitive applications. Additionally, simple designs are required, such as FMCW signal generation by the direct modulation of the current of a semiconductor laser. However, semiconductor lasers are inherently nonlinear, and the driving waveform needs to be optimized to generate linear FMCW signals. In this paper, we employ pre-distortion techniques to compensate for chirp nonlinearity, achieving frequency nonlinearities of 0.0029% for the down-ramp and the up-ramp at 55 kHz. Experimental results demonstrate a highly accurate LiDAR system with a resolution of under 5 cm, operating over a 210-m range through single-mode fiber, which corresponds to approximately 308 m in free space, towards meeting the requirements for long-range autonomous driving. Full article

A linear frequency modulation (LFM) signal and its corresponding de-chirp operation are one of the basic methods for wideband radar signal processing, which can reduce the burden of the radar system sampling rate and is more suitable for large-bandwidth signal processing. More importantly, most existing methods against interrupted sampling repeater jamming (ISRJ) are based on time–frequency (TF) or frequency domain analysis of the de-chirped signal. However, the above anti-ISRJ methods cannot be directly applied to multiple-input multiple-output (MIMO) radar with multiple beams, because the angular waveform (AW) in mainlobe directions does not possess the TF properties of the LFM signal. Consequently, this work focuses on the co-optimization of transmit beampattern and AW similarity in wideband MIMO radar systems. Different from the existing works, which only concern the space–frequency pattern of the transmit waveform, we recast the transmit beampattern and AW expressions for wideband MIMO radar in a more compact form. Based on the compact expressions, a co-optimization model of the transmit beampattern and AWs is formulated where the similarity constraint is added to force the AW to share the TF properties of the LFM signal. An algorithm based on the alternating direction method of multipliers (ADMM) framework is proposed to address the aforementioned problem. Numerical simulations show that the optimized waveform can form the desired transmit beampattern and its AWs have similar TF properties and de-chirp results to the LFM signal. Full article

In this paper, we obtain uncertainty principles associated with the linear canonical transform (LCT) of hypercomplex functions. First, we derive the uncertainty principle for hypercomplex functions in the time and LCT domains. Moreover, we exploit the uncertainty principle in two LCT domains. The lower bounds are related to the LCT parameters and the covariance, and the uncertainty principle presented herein is sharper than what has been presented in the existing literature.These tighter bounds can be obtained using hypercomplex chirp functions for a Gaussian envelope. Finally, we verify the validity of the uncertainty principles through some examples and discuss several potential applications of the new results in signal processing. Full article

A novel photonics-assisted technique for generating reconfigurable frequency hopping (FH) signals is proposed and demonstrated through optical comb filtering (OCF). The arithmetic progression of frequency difference between OCF passbands and optical frequency comb lines is exploited to enable wavelength selection controlled by an intermediate frequency signal, with ultra-wideband FH signals subsequently being generated through optical heterodyning. Comprehensive theoretical and numerical investigations are conducted, demonstrating the successful generation of diverse FH waveforms including 5-, 10-, and 25-level stepped frequency signals, Costas-coded patterns, as well as complex wideband signals such as 30 GHz linear frequency modulated and 24 GHz sinusoidal chirped waveforms. Critical system considerations including laser frequency stability, FH speed, and parameter optimization are examined. Wide tunable bandwidth exceeding 30 GHz, good stability, and inherent compatibility with photonic integration is achieved, showing significant potential for advanced applications in cognitive radio and modern radar systems where high-performance frequency-agile signal generation is required. Full article

To achieve precise detection of target geometric features, Ka/W/sub-terahertz band imaging radar systems with ultra-wide instantaneous bandwidth have been developed. Although dechirp-based receiver architectures allow for low-sampling-rate signal acquisition, they require precise linearity in chirp signals, often necessitating precompensation for nonlinear errors. While most research addresses polynomial-based error correction, periodic errors remain underexplored, despite their potential to obscure weak targets and introduce spurious ones. This paper proposes a novel software-based correction method that integrates neural networks and joint optimization strategies to correct periodic phase errors. The method first employs neural networks for frequency estimation, followed by phase-matching techniques to extract amplitude and phase data. Parameter estimation is refined using the Adaptive Moment Estimation (ADAM) algorithm and Limited-Memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS) optimization. Nonlinear errors are corrected via matched Fourier transforms. Simulations and experiments demonstrate that the proposed method effectively suppresses spurious targets and enhances the detection of weak targets, demonstrating strong robustness and practical applicability, thereby significantly enhancing the target detection performance of the ultra-wideband radar system. Full article

A bolt loosening detection method based on the summation coefficient of the absolute spectrum ratio technique is proposed to address the prevalent issue of bolt loosening in mechanical connections. This proposed method involves initially collecting vibration and rotation speed signals of the motor bolt connection structure, acquiring the baseline spectrum curve of a healthy structure and the spectrum curves of non-healthy structures under different degrees of bolt looseness through chirp Fourier transform (CFT). Subsequently, the spectrum ratio curves between healthy and non-healthy structures are calculated for different degrees of bolt loosening, and then the Summation Coefficient of Absolute Spectrum Ratio (SCASR) is defined to indicate the looseness. In the mathematical model, a linear relationship is observed between the SCASR and the frequency shift of the resonance peak. To standardize the results of bolt loosening detection, the SCASR could be divided by the number of points in the fixed frequency band to obtain the average of SCASR as the detection index for bolt loosening. Finally, a linear fitting equation is established between the bolt torque and the average of SCASR, so that the change in the average value can be used to determine whether the bolt is loose and evaluate the severity of bolt looseness. The detection performance of this proposed method has been effectively validated through both simulation and experiments. Experimental results indicate that the proposed method can effectively detect bolt loosening, particularly in its early stages, using low-frequency band data in three axes. Full article

By utilizing chirp-BOK (binary orthogonal keying) modulation into a troposphere scattering communication system, a lower demodulation threshold can be achieved with excellent linear frequency modulation properties in a strong noise and weak signal environment. Firstly, the bit error rate (BER) formula of chirp-BOK modulation over a Rayleigh fading channel was derived theoretically. Then, the BER performance with different chirp-BOK parameters were numerically calculated. In order to investigate the performance of chirp-BOK over deeping fading troposphere scattering link, a seven-path equal-delay Rayleigh fading model was employed. Finally, the system BER performance was simulated under different tap delay and time–bandwidth product parameters. The results demonstrate that when BER reaches ${10}^{-4}$, the optimal configuration of the system achieves a gains approximately from 1.7 dB to 10 dB compared to non-optimized configuration under different Path-Gain-Vector with varying tap delays. Full article

In complex and diverse practical application scenarios, the challenge of blind source separation under underdetermined and nonlinear conditions is often encountered. To address this challenge, this paper proposes an innovative underdetermined nonlinear bounded component analysis method. This method first employs Multivariate Nonlinear Chirp Mode Decomposition (MNCMD) to process and reconstruct the observed signals, transforming the original underdetermined problem into a positive definite problem. Subsequently, Gaussianization techniques are introduced as a means of nonlinear compensation, successfully converting the nonlinear model into an analyzable linear model, laying a solid foundation for subsequent signal separation. Finally, the signal is separated by the bounded component analysis method, which does not require the source signals to be independent of each other. To validate the effectiveness and superiority of the proposed algorithm, detailed simulation experiments were designed and implemented. The experimental results demonstrate that compared to traditional underdetermined blind source separation algorithms, the algorithm presented in this paper exhibits significant advantages in terms of universality, convergence speed, separation accuracy, and robustness. Furthermore, this paper successfully applies the algorithm to the blind extraction of fetal electrocardiogram (FECG) signals from real datasets. The experimental results show that the algorithm can rapidly and effectively extract clearer and more accurate FECG signals, demonstrating its great potential and value in practical applications. Full article

With the continuous development of synthetic aperture radar (SAR) jamming technology, low-power maneuvering repeater jammers are now flexible and can be located on multiple unmanned aerial vehicles (UAVs) and unmanned vehicles (UVs) at the same time, which greatly increases the difficulty of the anti-maneuvering repeater jamming method for spaceborne SAR. Due to the low-power transmission, the locations of the low-power repeater jammers and the protected areas in the imaged swath are relatively close in distance, while the transmission delay of the jamming is approximately equal to the pulse repetition interval (PRI). According to this phenomenon, an anti-maneuvering repeater jamming method using up- and down-chirp modulation is proposed in this paper. After alternately transmitting up- and down-chirp modulation signals, echoes of the jamming and the protected area are recorded in the same location within the echo-receiving window and are related to different chirp modulations. To remove the jamming echoes, de-chirping and frequency filtering are adopted after echo data segmentation. With jamming interference removal using frequency notch filtering, parts of the spectra corresponding to the desired echoes of the imaged swath are simultaneously removed. To recover the unwanted removed range spectra, linear prediction is introduced to improve the focusing quality. Finally, simulation results on both point and distributed targets validate the proposed anti-maneuvering repeater jamming method by using up- and down-chirp modulation. Full article

Modern radar technology requires high-quality signals and detection performance. However, traditional frequency-modulated continuous wave (FMCW) radar often has poor anti-jamming capabilities, and the high sampling rates associated with large time-bandwidth product signals can lead to increased system hardware costs and reduced data processing efficiency. This paper constructed a composite radar waveform based on noise frequency modulation (NFM) and linear frequency modulation (LFM) signals, enhancing the signal’s complexity and anti-jamming capability. Furthermore, a method for optimizing the processing of echo signals based on de-chirp and delay matching is proposed. The locally generated LFM signal is used to de-chirp the received echoes, resulting in a narrowband difference frequency noise signal. Subsequently, delay matching is performed in the fast time domain using the locally generated NFM signal according to the number of sampling points in the traversal processing period, allowing for the acquisition of target delay information. While reducing the analog-to-digital (A/D) sampling rate, the detection performance for wideband echo signals under high sampling rates is still maintained, with sidelobe levels and range resolution preserved. Accumulating this information in the slow time domain enables accurate target detection. The effectiveness of the proposed method is validated through simulation experiments. Full article

In the field of fiber-optic sensing, effectively reducing the noise of sensing spectra and achieving a high signal-to-noise ratio (SNR) has consistently been a focal point of research. This study proposes a deep-learning-based denoising method for fiber-optic sensors, which involves pre-processing the sensor spectrum into a 2D image and training with a cycle-consistent generative adversarial network (Cycle-GAN) model. The pre-trained algorithm demonstrates the ability to effectively denoise various spectrum types and noise profiles. This study evaluates the denoising performance of simulated spectra obtained from four different types of fiber-optic sensors: fiber Fabry–Perot interferometer (FPI), regular fiber Bragg grating (FBG), chirped FBG, and FBG pair. Compared to traditional denoising algorithms such as wavelet transform (WT) and empirical mode decomposition (EMD), the proposed method achieves an SNR improvement of up to $13.71 \mathrm{dB}$, an RMSE that is up to three times smaller, and a minimum correlation coefficient ($R^2$) of no less than 99.70% with the original high-SNR signals. Additionally, the proposed algorithm was tested for multimode noise reduction, demonstrating an excellent linearity in temperature response with a $R^2$ of 99.95% for its linear fitting and $99.74\%$ for the temperature response obtained from single-mode fiber sensors. The proposed denoising approach effectively reduces the impact of various noises from the sensing system, enhancing the practicality of fiber-optic sensing, especially for specialized fiber applications in research and industrial domains. Full article

When a synthetic aperture sonar (SAS) system operates under low-frequency broadband conditions, the azimuth range coupling of the point target reference spectrum (PTRS) is severe, and the high-resolution imaging range is limited. To solve the above issue, we first convert multi-receivers’ signal into the equivalent monostatic signal and then divide the equivalent monostatic signal into range subblocks and the range frequency subbands within each range subblock in order. The azimuth range coupling terms are converted into linear terms based on piece-wise linear approximation (PLA), and the phase error of the PTRS within each subband is less than π/4. Then, we use the chirp-z transform (CZT) to correct range cell migration (RCM) to obtain low-resolution results for different subbands. After RCM correction, the subbands’ signals are coherently summed in the range frequency domain to obtain a high-resolution image. Finally, different subblocks are concatenated in the range time domain to obtain the final result of the whole swath. The processing of different subblocks and different subbands can be implemented in parallel. Computer simulation experiments and field data have verified the superiority of the proposed method over existing methods. Full article

We demonstrate a low latency delay of a radio frequency (RF)–linear frequency-modulated (LFM) pulse by modulating it onto optical carriers from a Kerr comb and sending the signal through a concatenation of off-the-shelf linearly chirped fiber Bragg gratings (LC-FBGs) and chirped-and-sampled FBG (CS-FBG). We characterize the frequency response and latency of the LC-FBG and CS-FBG. Then, experimentally, the LFM pulse performance is characterized by measuring the peak sidelobe level (PSL) at the output of the tunable delay system. The experiment, performed with an LFM pulse of 1 GHz bandwidth at a 10 GHz center frequency, shows a PSL better than 34.4 dB, attesting to the high quality of the buffer RF transfer function. Thus, the proposed optical memory buffer architecture, utilizing compact devices based on a Kerr comb and FBGs, offers several benefits for delaying LFM pulses, including (i) a larger tunable delay range, (ii) low latency, (iii) wide bandwidth, and (iv) high PSL. Full article