This study addresses the challenge of retrieving traffic road visibility in the Internet of Video Things (IoVT). The complexity arises from the highly ill-posed inverse problem of estimating road visibility from video frames due to indirect inference, leading to ambiguity and multiple possible solutions. Initially, this study formulates the fog effects on images and discovers that the first and second eigenvalues of the observed image matrix closely approximate those of the airlight component under fog conditions. Based on this important discovery, a four-step framework is proposed for estimating road visibility. The framework includes defining persistent scatterers, formulating a singular value decomposition (SVD)-based method for background and airlight separation, extracting crucial physical features, and designing a hybrid convolutional long short-term memory (LSTM) network for accurate visibility estimation. Specifically, the foundational step defines persistent scatterers in visual scenes, followed by a sophisticated SVD-based method for background and airlight separation. Subsequently, physical features, including eigenvalue-based entropy and persistent scatterer intensity, are computed. The framework concludes with a novel hybrid convolutional LSTM network tailored for traffic road visibility estimation. To validate the methodology, three comparative research methods are introduced: one based on the Koschmieder law, another utilizing a convolutional neural network (CNN), and a third employing a deep LSTM approach. Results indicate correlation coefficients of 0.2417, 0.3325, 0.7930, and 0.9484 for the Koschmieder law-based, CNN, deep LSTM, and the proposed method, respectively, when compared to true visibility measurements. Furthermore, the averaged root mean square estimation errors are 6539 m, 9095 m, 1600 m, and 681 m for the Koschmieder law-based, CNN, deep LSTM, and the proposed method, respectively. The data and code for this study are available athttps://github.com/Z-H-XU/Benchmark-Visibilityto facilitate reproducibility within the research community.

This study explores the potential of using power lines to sense human activities. A model based on the Hertzian dipole approximation method is proposed to simulate the sensing mechanism, involving the calculation of the radiated field and the estimation of the Doppler frequency of the echoes. Simulations conducted with the proposed model demonstrate its effectiveness. Additionally, measurements performed with a universal software radio peripheral (USRP) follow the simulation results closely. These preliminary findings illustrate the feasibility of utilizing power lines for sensing human activities.

High-resolution imaging is an ever-popular research topic in the radar community. However, achieving higher resolution generally necessitates larger bandwidths, posing significant engineering challenges. To overcome these challenges, synthesizing a large bandwidth using shifting sub-bands across different center frequencies has emerged as a promising technique. This study explores the advantages and challenges of frequency modulated continuous wave (FMCW) radar imaging with shifting sub-bands. Methods to compensate for phase errors have been investigated to synthesize baseband signal. The imaging performance has been evaluated through both simulations and outdoor experiments using universal software radio peripheral (USRP) X410.

With the increasing number of radar-equipped vehicles in dense traffic situations, millimeter wave radars are suffering from serious interference problems. Therefore, this article presents a novel bi-level$l_{1}$optimization based approach for reducing interferences between automotive radars for range, velocity and angle measurement. Firstly, sparse difference analysis between the interfering signal and the target signal is investigated. According to the analysis results, one bi-level based signal optimization model is further derived by using$l_{1}$-norm penalized least squares. This bi-level optimization enables a trade-off between suppressing interference and preserving radar targets. Specifically, in the first$l_{1}$level, the interfering signal is first optimized as the “desired signal”, while the target is considered as “noise”. Meanwhile, sparse optimization is applied on the target signals in the frequency domain at the second$l_{1}$level. Finally, the iterated soft-thresholding algorithm is used to optimize the proposed model. In real road interference suppression experiments, the proposed method improves the signal to interference plus noise ratio (SINR) for the target from 5.06 dB to 19.26 dB in the range-Doppler domain and from 6.86 dB to 22.10 dB in the azimuth spatial domain.

This study investigates both fully and nonfully developed speckle models for wavelength-resolution synthetic aperture radar (SAR). The resolution of the wavelength-resolution SAR systems is on the order of wavelengths of the radar signal. Therefore, the equivalent number of independent scatterers per resolution cell is so small that the speckle is not fully developed. Based on the central limit theorem, the relationship between the number of scatterers and the nonfully developed speckle is first clarified in detail by using a characteristic function and moment generating function. Considering the surface scattering and volume scattering for the radar observations, both fully and nonfully developed speckles are demonstrated in wavelength-resolution SAR images by testing three probability density functions (pdfs), namely, K-distribution, Rayleigh, log-normal, and Weibull distributions. The use of airborne coherent all radio band sensing II (CARABAS-II) very high frequency (VHF) (20–90 MHz) SAR data for the nonfully developed speckle has been investigated. This study discovers that the speckle is not fully developed when the equivalent number of independent scatterers is less than eight. Wavelength-resolution SAR data exhibit nonfully developed speckles on regions of interest in heterogeneous surface scattering and volume scattering. In contrast, for homogeneous surfaces with specular scattering on lakes and some roads, the data follow a Rayleigh distribution due to systematic background thermal noise and very little reflected received signal. In this case, it corresponds to the absence of effective independent scatterers within the resolution cell.

Linear frequency modulated continuous wave (LFMCW) radar has been proven to be a highly reliable sensor to improve traffic safety. However, the increased use of LFMCW radars creates new challenges in mutual interference for automotive applications. In this contribution, an efficient approach is developed to reduce mutual interference by applying randomized sub-band spectra technique. Compared with state-of-the-art methods, the proposal shows superior performances.

A priori-knowledge based ship CFAR detection and determination algorithm is proposed in medium and high resolution SAR images. The algorithm first runs CFAR prescreening to get the coarse detection result, then the priori knowledge of the ships such as area, length and width is used for target discrimination. A sliding window with a certain size and a bright pixel number threshold is set, the window slides on the coarse detection image with a certain step, and the bright pixels in the sliding window are determined whether targets or clutter. If the number of bright pixels in the sliding window is larger than the bright pixel number threshold, then all the bright pixels in the sliding window will be determined as targets, otherwise clutter; finally, the Probability of False Alarm (PFA) of the whole algorithm is deduced. Using the algorithm, the false alarm rate (FAR) is greatly reduced while the targets can be insured detected. The simulation results prove the algorithm's effectiveness.

To improve traffic safety, millimeter wave radars have been widely used for sensing traffic environment. As radars also operate on a narrow small road and in the same frequency band, mutual interference between different automotive radars that arises cannot be easily reduced by frequency or polarization diversity. This letter presents novel orthogonal noise waveforms to reduce such neighboring interferences. First, the spectral density distribution function of the proposed waveforms is defined by using an optimized Kaiser function. Subsequently, the phases of the noise waveforms are formulated as a problem of phase retrieval and are explored. Thanks to nonuniqueness solutions, the proposed method generates the orthogonal signals with a good random phase diversity. The proposed method was tested on a representative scenario for interference reduction. The experimental results show that the proposed method can produce visually convincing radar images, and the signal-to-interference and noise ratio is better than the existing methods.

This study presents a novel robust method for Bayesian denoising of parallel magnetic resonance imaging (pMRI) images. For the first time, the authors’ proposal applies fields of experts (FoE), a filter‐based higher‐order Markov random field (MRF), to model the prior of the pMRI image statistics. The noise in pMRI data behaves to be non‐central Chi (nc‐ χ ) distributed. In practice, correlation between coils exists, resulting in that nc‐ χ distribution does not hold anymore and the spatially varying noise problem. Thus, preservation of fine textures requires to adapt locally the estimation. Therefore, more precisely, the noise is reduced by using a sliding window scheme. In each window, the likelihood probability function is accurately modelled from corrupted data by using an innovative Gaussian mixture model (GMM). The parameters of GMM are calculated by applying an iterative expectation maximisation approach. With the priors via the learned FoE model and the likelihood function via GMM, a maximum a posteriori (MAP) estimator is formulated. Then, the noise in the each window is filtered by applying an efficient non‐linear quasi‐Newton method to explore an optimal solution for the MAP estimator. Finally, experiments have been conducted on both the simulated and real data to compare the proposed model with some state‐of‐the‐art denoising methods. The experimental results demonstrate the robustness and effectiveness of the proposed denoising model.

Range sidelobe suppression in synthetic aperture radars (SARs) is conventionally realized using amplitude weighting with windowing functions in either time or frequency domains, which would result in a reduced signal-to-noise ratio (SNR) of the output. To counterbalance the loss, a system with more complexity and cost is needed, especially for spaceborne SAR. Fortunately, nonlinear frequency modulation (NLFM) chirp waveforms, which can shape the signal's power spectral density and offer radar matched filter output with lower sidelobes at no cost of reduced SNR, is a promising candidate. In this letter, the proof-of-principle experiment is presented to construct NLFM transmitting pulses using a real SAR system platform on the ground and analyze the characteristics of the pulses. Two approaches are developed to perform system-specific predistortion and the advantage of NLFM waveforms with better SNR is demonstrated using the theoretical derivation and experimental results. For spaceborne NLFM SAR, the effect of Doppler shift on NLFM SAR imaging cannot be neglected. Therefore, for further promoting the application of NLFM in SAR, one effective compensation approach is developed. All the experimental results and analysis validate the promising potential of NLFM for SAR application.

For synthetic aperture radar (SAR), a system impulse response with low sidelobes is very important because sidelobes may interfere with the nearby scatterers and contribute to multiplicative noise. It is well known that a nonlinear frequency-modulation (NLFM) chirp waveform can shape the signal's power spectral density and provide a radar matched filter output with lower sidelobes without loss of the signal-to-noise ratio when compared with the linear frequency-modulation chirp. These advantages make the NLFM waveform to be a promising candidate to improve the imaging quality for SAR. However, so far, to our knowledge, there is no real application of NLFM waveforms for SAR. This letter, for the first time, demonstrates the airborne SAR experiment using an NLFM waveform. In the underlying experiment, the construction of the NLFM signal is investigated and a modified range migration algorithm (RMA) is developed to adapt it for focusing the NLFM SAR data. Both simulation and experimental results exhibit the promising power of the NLFM chirp and show the accuracy of the proposed modified RMA.

In this letter, an improved full-aperture imaging algorithm for scanning synthetic aperture radar (ScanSAR) mode is proposed, which fills the data gaps between bursts through a linear-prediction-model-based aperture interpolation technique in a subaperture manner before azimuth compression. It can significantly suppress the spikes induced by periodical data gaps and, at the same time, enhance the signal-to-noise ratio of the obtained ScanSAR imagery. This approach has a great potential in the interferometric context. The effectiveness of the proposed approach is demonstrated by both simulated and real ScanSAR data with different types of terrain. All the experimental data were acquired by the C-band SAR system with a bandwidth of 200 MHz, which was developed by the Department of Space Microwave Remote Sensing System, Institute of Electronics, Chinese Academy of Sciences.

In order to enable a testbed for spaceborne scanning synthetic aperture radar (ScanSAR) mode, in this letter, a ScanSAR autofocusing approach for airborne platforms has been developed. Autofocusing algorithms, such as the phase gradient autofocus (PGA) algorithm, prove to be a useful postprocessing technique to get refocused synthetic aperture radar images. However, conventional stripmap PGA does not work in ScanSAR mode when the full-aperture approach is used, due to the periodic data gaps in each subswath. To solve this problem, we extend the stripmap PGA to ScanSAR with some modifications, mainly in the subaperture segmentation and phase error estimation steps. The performance of extended stripmap PGA is evaluated by an airborne ScanSAR data set containing different types of terrain, with a high spatial resolution up to 3.5 m in azimuth.