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A great interest has been directed towards high frequency surface wave radar (HFSWR), because it is applied as long‐range early warning and real‐time measurements of sea surface condition tools in maritime surveillance. However, the unwanted reflection from the ionosphere (ionosphere clutter) damages the consistent detection performance of HFSWR. Target detection and environmental information extraction from radar echoes are primarily determined by the extent to which the medium contaminates the transmitted signals. Although the ionospheric signal corruption mechanisms are complex and various, a number of eliminating contamination approaches have been developed in the last 50 years. The purpose of this paper is providing an overview of the past and current clutter theoretical models and clutter suppression techniques for HFSWR system. The experimental results involved in the complex long‐term ionosphere observation and ionosphere echo analysis are first discussed. Special attention is paid to the correlation between the signal degradation sources and eliminating clutter techniques. Furthermore, the clutter suppression methods are classified based on the design idea. The specific clutter suppression methods as well as its operation principle are briefly introduced. Finally, several research trends and open issues are presented, with emphasis on the need for proactively controlling the radar design cost and generally agreed‐upon standards. [ABSTRACT FROM AUTHOR]

Moving aerial targets are tracked in new era of integrated computing with intelligent gadgets as well as systems. Traditional matched filter-based techniques estimation performance would suffer significantly when tracking multiple targets, as weak target is obscured by strong target or strong sidelobes. This exploration propose novel method in radar optical communication handling in light of flying objective examination by recurrence transfer speed with mess concealment. The conventional static radar-based Boundary Element mmwave signal bandwidth and frequency development is used here for the high-profile aerial target detection. Following that, adaptive processing based low rant spatio-temporal with MIMO (multiple input multiple output) is used to suppress radar clutter. Signal-to-clutter-plus-noise ratio (SCPNR), detection accuracy, traffic control, congestion control, and mAP (mean average precision) all play a role in the experimental analysis. Simulation results demonstrate proposed techniques efficacy. The proposed technique attained SCPNR of 85%, detection accuracy of 92%, traffic control of 53%, congestion controlof 55%, mAP of 55%. [ABSTRACT FROM AUTHOR]

Deep learning models such as deep convolutional neural networks (DCNNs) image classifiers have achieved outstanding performance over the last decade. However, these models are mostly trained with high-quality images drawn from publicly available datasets such as ImageNet. Recently, many researchers have evaluated the impact of low-quality image degradations on the performance of different neural network-based image classifiers. But, most of these studies generate low-quality images by synthetic modification of the high-quality images. Besides, most of the studies employed various image processing techniques to remove the image degradations and trained the DCNNs again to achieve better performance. But it has since been discovered that such methods could not improve the classification accuracy of DCNNs. The robustness of DCNNs based image classifiers trained on low-quality images resulting from natural factors common in autonomous driving and other intelligent system settings was rarely studied over the recent years. In this paper, we proposed a canonical correlation analysis (CCA) based fusion of camera and radar features for improving the performance of DCNNs image classifiers trained on natural adverse weather data. CCA is a statistical approach that creates a highly discriminative feature vector by measuring the linear relationship between the camera and radar features. A spatial attention network was designed to re-weight the camera features before associating them with radar features in the CCA-feature fusion block. Our findings based on experimental evaluations have proven that, indeed, the performance of the DCNN models (i.e., Alex-Net and VGG-16-Net) is heavily affected by degradations arising from natural factors. Specifically, the DCNN models are more affected by the degradations arising from rainfall, foggy and nighttime conditions using Radiate and Carrada datasets. However, the proposed fusion frameworks have improved the performance of the individual sensing modalities significantly. The radar data has helped substantially in enhancing the fusion performance, mainly using rainfall data where the camera data is heavily affected. [ABSTRACT FROM AUTHOR]

The detection of small targets within the background of sea clutter is a significant challenge faced in radar signal processing. Small target echoes are weak in energy, and can be submerged by sea clutter and sea spikes, which are caused by overturning waves and breaking waves. This severely affects the radar target detection performance. This paper proposes a smoothed pseudo-Wigner–Ville distribution–singular value decomposition (SPWVD-SVD) method for sea clutter suppression. This method determines the instantaneous frequency range of the target by contrasting the time–frequency characteristics of the sea spike and the target. Subsequently, it employs a singular value difference spectrum to reduce the rank of the Hankel matrix, thereby reducing the computational burden of the instantaneous frequency estimation step in the experiment. Based on the instantaneous frequency range of the target in the time–frequency domain, the singular values of the target signal are retained, while the singular values of clutter are set to zero. This process accomplishes the reconstruction of radar echo signals and effectively achieves the suppression of sea clutter. The suppression effect is verified using simulation data alongside ten sets of Intelligent Pixel processing X-band (IPIX) radar data against the background of sea spikes. By contrasting the clutter amplitudes before and after suppression, the SPWVD-SVD algorithm demonstrated an average clutter suppression of 15.06 dB, which proves the effectiveness of the proposed algorithm in suppressing sea clutter. [ABSTRACT FROM AUTHOR]

One of the most important problems in traffic management is evaluating the amount of traffic and the velocity of vehicles passing through transportation ways and main streets. In this study, the traffic load is measured, and the speed of cars is estimated using the 24 GHz FMCW radar. In this process, an antenna is installed to record all the targets from the side of the highway. Next, the moving vehicles are classified according to their type, based on which the traffic load is provided to the user in each highway lane. Due to the radial velocity, low Doppler frequency, and variability with time, some challenges will emerge, for which two solutions will be provided. This paper describes the speed of cars and the amount of congestion in each highway lane presented by two new methods. Finally, with the process of canceling clutters observed by radar and detecting possible errors, a highway statistic is presented as a table. [ABSTRACT FROM AUTHOR]

A multi-hypothesis marginal multi-target Bayes filter for heavy-tailed observation noise is proposed to track multiple targets in the presence of clutter, missed detection, and target appearing and disappearing. The proposed filter propagates the existence probabilities and probability density functions (PDFs) of targets in the filter recursion. It uses the Student's t distribution to model the heavy-tailed non-Gaussian observation noise, and employs the variational Bayes technique to acquire the approximate distributions of individual targets. K-best hypotheses, obtained by minimizing the negative log-generalized-likelihood ratio, are used to establish the existence probabilities and PDFs of targets in the filter recursion. Experimental results indicate that the proposed filter achieves better tracking performance than other filters. [ABSTRACT FROM AUTHOR]

The human visual attention system plays an important role in infrared target recognition because it can quickly and accurately recognize infrared small targets and has good scene adaptability. This paper proposes an infrared small target detection method based on an attention mechanism, which consists of three modules: a bottom-up passive attention module, a top-down active attention module, and decision feedback equalization. In the top-down active attention module, given the Gaussian characteristics of infrared small targets, the idea of combining knowledge-experience Gaussian shape features is applied to implement feature extraction, and quaternion cosine transform is performed to achieve multi-dimensional fusion of Gaussian shape features, thereby achieving complementary fusion of multi-dimensional feature information. In the bottom-up passive attention module, considering that the difference in contrast and motion between the target and the background can attract attention easily, an optimal fast local contrast algorithm and improved circular pipeline filtering are adopted to find candidate target regions. Meanwhile, the multi-scale Laplacian of the Gaussian filter is adopted to estimate the optimal size of the infrared small target. The fast local contrast algorithm based on box filter acceleration and structure optimization is employed to extract local contrast features, and candidate target regions can be obtained by using an adaptive threshold. Besides, the mean gray, target size, Gaussian consistency, and circular region constraint are used in pipeline filtering to extract motion regions, and the false-alarm rate is reduced effectively. Finally, decision feedback equalization is adopted to obtain real targets. Experiments are conducted on some real infrared images involving complex backgrounds with sea, sky, and ground clutters, and the experimental results indicate that the proposed method can achieve better detection performance than conventional baseline methods, such as RLCM, ILCM, PQFT, MPCM, and ADMD. Also, mathematical proofs are provided to validate the proposed method. [ABSTRACT FROM AUTHOR]

The Dual-Frequency Precipitation Radar (DPR), which consists of a Ku-band precipitation radar (KuPR) and a Ka-band precipitation radar (KaPR) on board the GPM Core Observatory, cannot observe precipitation at low altitudes near the ground contaminated by surface clutter. This near-surface region is called the blind zone. DPR estimates the clutter-free bottom (CFB), which is the lowest altitude not included in the blind zone, and estimates precipitation at altitudes higher than the CFB. High CFBs, which are common over mountainous areas, represent obstacles to detection of shallow precipitation and estimation of low-level enhanced precipitation. We compared KuPR data with rain gauge data from Da-Tun Mountain of northern Taiwan acquired from March 2014 to February 2020. A total of 12 cases were identified in which the KuPR missed some rainfall with intensity of .10 mm h21 that was observed by rain gauges. Comparison of KuPR profile and ground-based radar profile revealed that shallow precipitation in the KuPR blind zone was missed because the CFB was estimated to be higher than the lower bound of the range free from surface echoes. In the original operational algorithm, CFB was estimated using only the received power data of the KuPR. In this study, the CFB was identified by the sharp increase in the difference between the received powers of the KuPR and the KaPR at altitude affected by surface clutter. By lowering the CFB, the KuPR succeeded in detection and estimation of shallow precipitation. SIGNIFICANCE STATEMENT: The Dual-Frequency Precipitation Radar (DPR) on board the GPMCore Observatory cannot capture precipitation in the low-altitude region near the ground contaminated by surface clutter. This region is called the blind zone. The DPR estimates the clutter-free bottom (CFB), which is the lower bound of the range free from surface echoes, and uses data higher than CFB. DPR consists of a Ku-band precipitation radar (KuPR) and a Ka-band precipitation radar (KaPR). KuPR missed some shallow precipitation more than 10 mm h21 in the blind zone over Da-Tun Mountain of northern Taiwan because of misjudged CFB estimation. Using both the KuPR and the KaPR, we improved the CFB estimation algorithm, which lowered the CFB, narrowed the blind zone, and improved the capability to detect shallow precipitation. [ABSTRACT FROM AUTHOR]

In a radar detection system, multiple pulse (MP) transmission is used to improve detection performance compared to the single pulse case by integrating the echoes of pulses at reception. In this paper, we derive closed-form expressions of the probability of false alarm PFA of the cell averaging-constant false alarm rate, greatest-of CFAR, and smallest-of CFAR detectors considering a homogeneous gamma distributed radar clutter applied to the MP case. Expressions are given by analytical formulas for a positive real shape parameter which correspond to a real situation and are validated by comparing them in terms of the detection threshold calculated values T, to the results obtained by means of Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

The Doppler spectra of sea echoes, which contain abundant information on floating scatterers, are important for exploring the characteristics of sea clutter. Using sea clutter data at low grazing angles observed by a coherent ultra-high frequency (UHF) radar located on Lingshan Island in the Yellow Sea, China, this study conducted detailed research on the characteristics of Doppler spectra with multiple ocean parameters, including grazing angle, significant wave height (SWH), and wave directions. The effect of sea echoes with different local normalized intensities on short-time Doppler spectra was further studied. The results indicate that with increasing sea states, the bimodal behavior of Doppler spectra, an evident phenomenon of Bragg scattering, gradually weakens. The frequency shifts of the mean spectra increased linearly with increasing SWH and wind speed, decreased linearly with increasing grazing angle, and decreased with the cosine value of the relative wave direction angles. In comparison, frequency shifts of the short-time spectra increased with increasing sea states and local echo intensities but fluctuated around a fixed value after reaching a certain extent. For spectral widths, the grazing angle is a significant influencing factor, with its broadening trend evident with a decrease in the grazing angle, whereas other ocean parameters, such as wave direction and wind direction, have no apparent influence. Considering the major contributions of the parameters, semi-empirical models for the mean spectral frequency shifts, mean spectral widths and short-time spectral frequency shifts were proposed. By verifying the measured data and predicted results, the models exhibited good prediction accuracy and applicability. The proposed inferences and models are helpful for understanding low grazing angle UHF-band sea clutter characteristics and improving target detection algorithms in offshore areas. These findings supplement previous studies on sea clutter Doppler spectra. [ABSTRACT FROM AUTHOR]

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Manipulations of multiple carrier frequencies are especially important in a variety of fields like radar detection and wireless communications. In conventional radio-frequency architecture, the multi-frequency control is implemented by microwave circuits, which are hard to integrate with antenna apertures, thus bringing the problems of expensive system and high power consumption. Previous studies demonstrate the possibility to jointly control the multiple harmonics using space-time-coding digital metasurface, but suffer from the drawback of inherent harmonic entanglement. To overcome the difficulties, we propose a multi-partition asynchronous space-time-coding digital metasurface (ASTCM) to generate and manipulate multiple frequencies with more flexibility. We further establish an ASTCM-based transmitter to realize wireless communications with frequency-division multiplexing, where the metasurface is responsible for carrier-wave generations and signal modulations. The direct multi-frequency controls with ASTCM provides a new avenue to simplify the traditional wireless systems with reduced costs and low power consumption. The authors present a multi-partition asynchronous space-time-coding digital metasurface (ASTCM) to control multifrequency waves; they demonstrate its capabilities by sending eight pictures over distinct frequencies using this wireless transmitter. The proposed work shows potential in enhancing microwave systems with improved power efficiency and cost-effectiveness. [ABSTRACT FROM AUTHOR]

Inversion of atmospheric ducts is of great importance in the field of performance evaluation for radar and communication systems. Since the model parameters in machine learning play a crucial role in prediction performance, this paper develops a random forest (RF) model integrated with Bayesian optimization (BO) called BO-RF for atmospheric duct prediction, and the BO is adopted to determine appropriate model parameters during the training process. In addition, the K-fold cross-validation (CV) method is also incorporated into the model to obtain the best model partition and overcome the overfitting problem. To test the performance of the proposed model, the results obtained by the BO-RF are compared with other commonly used methods, such as classical RF, extreme gradient boosting (XGBoost) with/without BO, and K-nearest neighbor (KNN) with/without BO. Comparisons demonstrate that BO-RF has the best accuracy and anti-noise ability for the estimation of duct parameters. [ABSTRACT FROM AUTHOR]

Non‐sidelooking airborne radar encounters significant non‐stationary and heterogeneous clutter environments, resulting in a severe shortage of samples. Sparse recovery‐based space‐time adaptive processing (SR‐STAP) methods can achieve good clutter suppression performance with limited samples. Nonetheless, grid‐based SR‐STAP algorithms encounter off‐grid effects in non‐sidelooking arrays, which can severely degrade the clutter suppression performance. In this study, the authors propose a novel gridless SR‐STAP method in the continuous spatial‐temporal domain to address the issue of off‐grid effects. Inspired by the fact that sparse Bayesian learning (SBL) framework implicitly performs a structured covariance matrix estimation, the authors reparameterise its cost function to directly estimate the block‐Toeplitz structured matrix from the measurements in a gridless manner. Since the proposed cost function is non‐convex, we utilise a majorisation‐minimisation‐based iterative procedure to estimate the clutter covariance matrix. Finally, using the standard concept of semidefinite programming, the authors derive a convex gridless implementation of the SBL cost function for uniformly sampled radar systems. Extensive simulation experiments demonstrate the exceptional clutter suppression and target detection performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The problem of multi-detection multi-target tracking (MDMTT) using over-the-horizon radar in dense clutter environment is studied in this paper. The biggest challenge of MDMTT is the 3-dimensional multipath data association among measurements, detection models and targets. In particular, a lot of clutter measurements are generated in dense clutter environment, which increase the computational burden of 3-dimensional multipath data association greatly. A measurement based dimension descent association (DDA) algorithm is proposed to solve the 3-dimensional multipath data association, which decomposes the 3-dimensional multipath data association into two 2-dimensional data associations. The proposed algorithm can reduce the computational burden compared with the optimal 3-dimensional multipath data association and the computational complexity is analyzed. Besides, a time extension method is designed to detect the new-born targets that appear in the tracking scene, which is based on the sequential measurements. The convergence of the proposed measurement based DDA algorithm is analyzed. The estimation error can convergence to 0 as the number of Gaussian mixtures tends to infinity. The effectiveness and rapidity of the measurement based DDA algorithm are demonstrated by the comparative simulation with the previously proposed algorithms. • Multi-detection multi-target tracking using OTHR is studied by dimension descent. • The detection of new-born targets with unknown prior knowledge is considered. • The convergence of the novel multi-target tracking algorithm is analyzed. • The labeled GMPHD is proposed to reduce the computational burden. [ABSTRACT FROM AUTHOR]

As the localized microbursts or flooding occur frequently due to the unusual weather phenomena, it is the very urgent and important problem to detect these weather hazards using the short range weather radar networks. For a short range weather radar of this purpose, the appropriate method is essential to verify the algorithm performance of this system. Therefore, in this paper, the efficient method to generate a short range weather radar signal was proposed for simulation of various weather phenomena by changing Doppler spectrum parameters. Using the proposed spectrum modeling method, both the strong clutter of a ground surface and man-made structures and the moving clutter such as birds can be easily simulated which often occur in a short range weather radar operation environment. In this paper, various weather signals with typical clutters were simulated using the proposed method and the results were analyzed. [ABSTRACT FROM AUTHOR]

As the Artificial Intelligence of Things (AIOT) and ubiquitous sensing technologies have been leaping forward, numerous scholars have placed a greater focus on the use of Impulse Radio Ultra-Wide Band (IR-UWB) radar signals for Region of Interest (ROI) population estimation. To address the problem concerning the fact that existing algorithms or models cannot accurately detect the number of people counted in ROI from low signal-to-noise ratio (SNR) received signals, an effective 1DCNN-LSTM model was proposed in this study to accurately detect the number of targets even in low-SNR environments with considerable people. First, human-induced excess kurtosis was detected by setting a threshold using the optimized CLEAN algorithm. Next, the preprocessed IR-UWB radar signal pulses were bundled into frames, and the resulting peaks were grouped to develop feature vectors. Subsequently, the sample set was trained based on the 1DCNN-LSTM algorithm neural network structure. In this study, the IR-UWB radar signal data were acquired from different real environments with different numbers of subjects (0–10). As indicated by the experimental results, the average accuracy of the proposed 1DCNN-LSTM model for the recognition of people counting reached 86.66% at ROI. In general, a high-accuracy, low-complexity, and high-robustness solution in IR-UWB radar people counting was presented in this study. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the research of fault arc detection technology, proper signal denoising can greatly improve the recognition ability of the arc fault detection algorithm. Therefore, this study proposes a current signal enhancement algorithm based on improved spectral subtraction from the statistical law of electrical noise in a low-voltage distribution system and the principle of the arc fault detection algorithm. According to the results of the arc fault detection algorithm, the algorithm selects an appropriate reference current as the basis for noise estimation and further assumes that the types and quantities of power loads are relatively fixed and the current noise is relatively stable in a certain power consumption period in the same power consumption place, so as to obtain the current noise in the current power consumption environment, and then uses improved spectral subtraction to realize the real-time noise reduction of current signals required by the arc fault detection algorithm. The experimental results show that this method and the arc fault detection algorithm complement each other, and the processing of current signal is more targeted, which can make the selected arc fault characteristics more different between the normal state and fault state, eliminate the interference of high-frequency clutter in current signal on arc fault characteristics and greatly improve the sensitivity of the original detection algorithm on the premise of unchanged detection reliability. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of estimating the parameters of heavy-tailed sea clutter in high-resolution radar, when the clutter is modeled by the correlated Pareto type II distribution. Existing estimators based on the maximum likelihood (ML) approach, integer-order moments (IOM) approach, fractional-order moments (FOM), and log-moments (log-MoM) have shown to be sensitive to changes in data correlation. In this work, we resort to a deep learning (DL) approach based on a multi-headed architecture to overcome this problem. Offline training of the artificial neural networks (ANN) is carried out by using several combinations of the clutter parameters, with different correlation degrees. To assess the performance of the proposed estimator, we resort to Monte Carlo simulation, and we observed that it has superior performance over existing approaches in terms of estimation mean square error (MSE) and robustness to changes of the clutter correlation coefficient. [ABSTRACT FROM AUTHOR]

Airborne radar plays a crucial role in detecting aerial targets, but its detection capability can be significantly affected by strong ground clutter when operating in down-looking mode. The range-Doppler (RD) results of ground clutter in airborne radar may overlap the range-Doppler results of the targets, causing target occlusion and rendering them undetectable. In addition to target detection, accurate extraction of target parameters is also of great importance. Due to the high-speed movement of the airborne platform, targets may exhibit range-Doppler ambiguity, necessitating the use of appropriate methods to resolve the range-Doppler ambiguity. Aiming at the problem of target occlusion by clutter with range-Doppler two-dimensional ambiguity during airborne radar down-looking target detection, this paper conducts a study on the detection of airborne radar down-looking targets based on orthogonal signals combined with pulse repetition frequency (PRF) staggered transmission. An optimization method is employed to design multiple sets of PRF values, aiming to reduce the radar blind zone. The range-Doppler two-dimensional ambiguity of targets is resolved using multiple sets of PRF, and coherent processing is performed on the corresponding echoes using the transmission intervals, maximizing the utilization of radar transmission energy. This approach enhances the signal-to-noise ratio (SNR) and improves the detection performance and parameter estimation accuracy, particularly for low SNR targets. The superiority of the proposed approach is demonstrated through simulation experiments. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) can provide high-resolution and large-scale maritime monitoring, which is beneficial to ship detection. However, ship-detection performance is significantly affected by the complexity of environments, such as uneven scattering of ship targets, the existence of speckle noise, ship side lobes, etc. In this paper, we present a novel anomaly-based detection method for ships using feature learning for superpixel (SP) processing cells. First, the multi-feature extraction of the SP cell is carried out, and to improve the discriminating ability for ship targets and clutter, we use the boundary feature described by the Haar-like descriptor, the saliency texture feature described by the non-uniform local binary pattern (LBP), and the intensity attention contrast feature to construct a three-dimensional (3D) feature space. Besides the feature extraction, the target classifier or determination is another key step in ship-detection processing, and therefore, the improved clutter-only feature-learning (COFL) strategy with false-alarm control is designed. In detection performance analyses, the public datasets HRSID and LS-SSDD-v1.0 are used to verify the method's effectiveness. Many experimental results show that the proposed method can significantly improve the detection performance of ship targets, and has a high detection rate and low false-alarm rate in complex background and multi-target marine environments. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of tracking multiple extended targets in three-dimensional space. We propose the Gaussian process Gaussian mixture probability hypothesis density (GP-PHD) filter, which is capable of tracking multiple extended targets with complex shapes in the presence of clutter. Our approach combines the Gaussian process regression measurement model with the probability hypothesis density filter to estimate both the kinematic state and the shape of the targets. The shape of the extended target is described by a 3D radial function and is estimated recursively using the Gaussian process regression model. Furthermore, we transform the recursive Gaussian process regression problem into a state estimation problem by deriving a state space model such that the estimation of the extent can be integrated into the kinematic part. We derive the predicted likelihood function of the PHD filter and provide a closed-form Gaussian mixture implementation. To evaluate the performance of the proposed filter, we simulate a typical extended target tracking scenario and compare the GP-PHD filter with the traditional Gamma Gaussian Inverse-Wishart PHD (GGIW-PHD) filter. Our results demonstrate that the proposed algorithm outperforms the GGIW-PHD filter in terms of estimating both kinematic states and shape. We also investigate the impact of the measurement rates on both filters; it is observed that the proposed filter exhibits robustness across various measurement rates, while the GGIW-PHD filter suffers under low-measurement-rate conditions. [ABSTRACT FROM AUTHOR]

On Jupiter's largest moon Ganymede, we focus on the southern region of Nippur Sulcus, including the Epigeus palimpsest crater. This region displays geologically recent terrains and has good coverage of optical and near infrared remote sensing data, primarily returned by the NASA Galileo mission. We first use high‐resolution optical imagery to carry out a detailed geological mapping. We combine hyperspectral data to infer the surface composition of this area. We then apply two theoretical models. The first is a self‐similar clustering that uses the mapping of the grooves present in this region to infer the thickness of the ice shell. A second model is used to estimate the radar clutter noise induced by the estimated local topography, which is key for constraining future radar measurements. Based on all findings, we provide a possible evolutionary history of the Nippur Sulcus and Epigeus areas. This exercise shows the potential of an interdisciplinary data analysis combining wide‐ranging scientific objectives such as geology, surface composition, and geophysics in view of the future close exploration that will be conducted on Ganymede by the JUICE spacecraft. Our work combines multiple analysis techniques that could be applied to other regions of interest and will prove crucial when JUICE data become available. Plain Language Summary: On Jupiter's largest moon Ganymede, we focus on the region of Nippur Sulcus, including the Epigeus palimpsest crater covered by optical and near infrared remote sensing data acquired in the past by the NASA Galileo mission, to carry out a detailed geological and compositional analysis of this region. We then apply two theoretical models focusing on the estimation of the grooves' depth and on the radar clutter noise induced by the local topography to show how an in‐depth analysis combining geology, surface composition, and geophysics could achieve a broader understanding of a given region of interest. For high‐priority regions of interest to be studied by JUICE in detail, such a multidisciplinary analysis could ultimately reveal connections between the surface and fluid subsurface. Key Points: We focus on the Nippur Sulcus region on Ganymede to perform a detailed geological and compositional analysis of this regionNippur's grooves have a composition similar to that of small fresh craters and may penetrate 125–135 km into the icy crustTo reveal potential links between the surface and subsurface liquid pockets, clutter‐induced noise must be treated adequately in radar data [ABSTRACT FROM AUTHOR]

Passive bistatic radar research is essential for accurate 3D target tracking, especially in the presence of missing or low-quality bearing information. Traditional extended Kalman filter (EKF) methods often introduce bias in such scenarios. To overcome this limitation, we propose employing the unscented Kalman filter (UKF) for handling the nonlinearities in 3D tracking, utilizing range and range-rate measurements. Additionally, we incorporate the probabilistic data association (PDA) algorithm with the UKF to handle cluttered environments. Through extensive simulations, we demonstrate a successful implementation of the UKF-PDA framework, showing that the proposed method effectively reduces bias and significantly advances tracking capabilities in passive bistatic radars. [ABSTRACT FROM AUTHOR]

For staring infrared sensors in geostationary orbit, the clutter caused by the high-frequency jitter and low-frequency drift of the sensor line-of-sight (LOS) is the impact of background features, sensor parameters, LOS motion characteristics, and background suppression algorithms. In this paper, the spectra of LOS jitter caused by cryocoolers and momentum wheels are analyzed, and the time-related factors such as the jitter spectrum, the detector integration time, the frame period, and the temporal differencing background suppression algorithm are considered comprehensively; they are combined into a background-independent jitter-equivalent angle model. A jitter-caused clutter model in the form of multiplying the background radiation intensity gradient statistics by the jitter-equivalent angle is established. This model has good versatility and high efficiency and is suitable for the quantitative evaluation of clutter and the iterative optimization of sensor design. Based on satellite ground vibration experiments and on-orbit measured image sequences, the jitter-caused clutter and drift-caused clutter models are verified. The relative deviation between the model calculation and the actual measurement results is less than 20%. [ABSTRACT FROM AUTHOR]

Eliminating common-mode clutter in data is one of the key aspects of road sensing with GPR. Common-mode interference can occur as a result of multipath propagation of an electromagnetic signal when the reflected signal from the same object arrives at the receiver from different directions and with different delays. Similar phenomena also occur when using antennas raised above the surface due to multiple reflections between the air–surface interface and the antenna. These interferences can significantly distort the data received by the GPR and interfere with the accurate determination of the parameters of the roadway. Therefore, the elimination of common-mode clutter is an important task to improve the quality of the obtained results. In this paper, we consider a method for filtering common-mode clutter in the radar data of the multichannel GPR "Terrazond", which were obtained by sounding a test section of a highway. The results obtained during filtering can then be used to determine the thickness of the pavement layers using approaches that take into account the signal delay determined by the amplitude jump, for example, the common point method or if the permittivity of each layer is known. The obtained thicknesses of pavement layers are compared with the results obtained during core drilling by the Russian Road Research Institute. [ABSTRACT FROM AUTHOR]

SAR clutter statistical modelling research has been vital for interpreting SAR images. Early studies employed statistical modelling or electromagnetic calculation to generate radar clutters, which had high computational complexity and struggled to accurately characterize ground clutter in complex scenarios. To address these challenges, more attentions were paid to the non-parametric model recently. A large number of generative models were presented previously. However, little work was devoted to the complex-valued radar images. To fill the gap, a new dual-channel generative adversarial network is proposed in this paper. The proposed architecture is composed of two generative adversarial networks. The real and the imaginary components can be then generated individually. The phase information can be preserved accordingly. In addition, a new evaluation metric, the Mixed Classification Curve (MCC) is proposed to assess the simulated clutter. A generic neural network is first built. It is then trained by different combinations of the original data and the generated one. The trained model is then used to predict the identification of the query. The MCC is finally formed by the classification rate across different combinations of data. Multiple experiment results verify the effectiveness and advantage of the proposed method. [ABSTRACT FROM AUTHOR]

Five different data fusion techniques (multiple linear regression (MLR), high-pass filtering (HPF), intensity hue saturation (IHS), wavelet transformation (WT) and the hybrid method WT + IHS) have been applied to model the aboveground forest biomass (AGB) in this study. The RapidEye multispectral image and the PALSAR radar image were used in research as sources of remote sensing data. Five models for estimating forest AGB were built and analysed using data from test area in Chernihiv region (Ukrainian Polissya). Correlation and min–max accuracy have been calculated for each model to measure the model performance. Among all the data fusion approaches used in the study, the high-pass filtering method has shown the greatest efficiency. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the development of the Global Navigation Satellite System (GNSS) and regional augmentation systems, the influence of the ionosphere on GNSS signals is becoming increasingly important. It is also of particular interest to retrieve the electron density distribution from GNSS observations. We have successfully developed a four‐dimensional variational assimilation scheme, the Neustrelitz Electron Density Assimilation Model (NEDAM), and verified NEDAM by a simulation study using a European ground‐based GNSS network. The performance of NEDAM is validated using two ionosondes and COSMIC‐1 radio occultation observations during the September 2017 geomagnetic storm period. The critical frequency of the F2 layer in NEDAM is much more accurate than that of a physics‐based model driven by observed geophysical indices or the Neustrelitz Electron Density Model (NEDM), when compared to data from two ionosondes. During the storm, the root mean square error of the F2‐layer critical frequency with respect to the two ionosondes is improved by 0.54 and 0.42 MHz, respectively. We also compare two co‐located electron density profiles from the COSMIC‐1 mission with NEDAM and NEDM. It is found that NEDAM is able to reconstruct well not only the peak density but also the peak density height, which is missing in the previous research. Plain Language Summary: With the development of ground‐ and space‐based observation techniques of the upper atmosphere, the capability of monitoring the variation of the Earth's ionosphere is increased drastically. However, due to the lack of three‐dimensional observations with a good coverage, reliable and well defined scheme is required for extracting three‐dimensional snapshots of the Earth's ionospheric electron density. This work presents an advanced four‐dimensional variational scheme NEDAM in the region of 60–20,000 km above the Earth's surface. The work also validates the tomography results with multiple ionospheric observations from satellites and ground‐based radar instruments and compares with empirical and physical models. It is usually a challenge for assimilative models to reproduce the ionospheric parameters such as the peak density height accurately during geomagnetic storms. The performance of NEDAM is validated against two ionosondes and COSMIC radio occultation observations during the September 2017 geomagnetic storm period. The critical frequency of F2‐layer in NEDAM is much more accurate than that of NEDM with respect to the two ionosondes in the region of interests. The developed method will be applied in near real‐time to space weather monitoring, GPS positioning and navigation, as well as to improve our understanding of the morphology and dynamics of the ionosphere. Key Points: A 4D‐Var‐based new global ionosphere electron density assimilation model Neustrelitz Electron Density Assimilation Model (NEDAM) is presentedThe background covariance matrix is approximated by a Gaussian‐Markov random field and the observation covariance is improvedThe accuracy of NEDAM is evaluated using simulated Global Navigation Satellite System observations and two ionosondes and COSMIC‐1 data [ABSTRACT FROM AUTHOR]

A recent survey has revealed that radar researchers still encounter difficulties to develop detectors that cope with non-Gaussian backgrounds. On another note, in real-world applications, many modeling research papers show a high agreement between radar sea clutter data and the Weibull distribution. In this paper, we propose and analyze the Quantile Matching Constant False Alarm Rate (QM-CFAR) detector for a Weibull background. Specifically, assuming a non-stationary Weibull clutter with the presence or not of interfering targets, the Quantile Matching (QM) and the Maximum Likelihood Estimator (MLE) are concomitantly used to allow the proposed detector to perform robustly in multiple target situations with a priori unknown Weibull parameters. By that means, we first rank order the reference samples to select quantile information that shares the same clutter parameters as the Cell Under Test and eliminate any outliers within the data. Then, we resort to the QM and the MLE to get the parameters. Finally, we carry out target decision-making. The subsequent CFAR detection threshold allows then fixed censoring of the upper end of the reference window. Monte-Carlo simulations show that, compared to recent existing CFAR algorithms, the QM-CFAR detector provides robust and accurate estimates of the Weibull distribution parameters and achieves less degradation of the P D (Detection Probability) in multiple target situations. [ABSTRACT FROM AUTHOR]

In high frequency (HF) hybrid sky‐surface wave radar, the first‐order sea clutter broadening is severe under the action of ionospheric phase disturbance and bistatic angles. In this study, a spread sea clutter suppression method based on prior knowledge and joint domain localised (JDL) algorithm is proposed. Firstly, the first‐order sea clutter broadening mechanisms and model under the action of ionospheric phase disturbance and bistatic angles are introduced. Secondly, the space‐time coupling characteristics of the first‐order sea clutter is analysed. Then in order to better suppress clutter or avoid target information loss, the spread sea clutter suppression method based on space time adaptive processing is proposed. In the proposed method, the space‐time distribution characteristics and simulation model of the first‐order sea clutter is used as the prior knowledge into JDL algorithm to improve the sea clutter suppression performance. Finally, the performance of sea clutter suppression based on JDL algorithm is simulated and analysed. And the proposed spread sea clutter suppression method based on prior knowledge and JDL algorithm is proved by measured data. [ABSTRACT FROM AUTHOR]

This letter addresses high-resolution sea clutter modelling. Radar clutter modelling is essential to develop constant false alarm rate (CFAR) detection schemes. In this paper, we propose a compound model by the use of the inverted exponentiated Rayleigh (IER) distribution as texture component. The probability density function (PDF) of the compound inverted exponentiated Rayleigh (CIER) distribution is a function of three parameters including the thermal noise power. To assess the modelling performance of the CIER distribution, several tests have been performed using the Intelligent PIXel Processing Radar (IPIX) database where the fitted PDFs and the complimentary cumulative density functions (CCDFs) of the CIER, the well-known compound K and the compound inverse Gaussian (CIG) distributions are compared with the real PDF and CCDF. The results obtained validate the ability of the CIER distribution to model high-resolution sea clutter. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Parameter estimation is significant to prediction and estimation in the field of radar clutter characteristics. Therefore, it is necessary to study the problem of parameter estimation. The K-distribution is a commonly used model in sea clutter, which is a two-parameter model with shape parameters and scale parameters. The value of the shape parameters should be greater than 0. Moment estimation is usually used to estimate the parameters of the K-distribution. It overcomes the disadvantage of large computation compared with the maximum likelihood estimation method. However, the moment estimation usually uses two different order origin moments to solve the parameters. The joint solution of different order will cause large calculation errors, and sometimes the shape parameter is estimated to be less than 0. In the origin moment expression, the order k can be regarded as a continuous variable. By calculating the relationship between the k-order origin moment and its derivative, a parameter estimation method based on the origin moment derivative is proposed. The estimation efficiency and accuracy are compared with some moment estimation methods. Both simulation data and measured clutter data show that this method can achieve 100% estimation efficiency, can obtain higher estimation accuracy, and can also avoid the situation where the estimated value of the shape parameter is less than 0. Using the same idea to estimate the parameters in the two-parameter models, log–normal and Weibull distribution, we can also obtain the parameters with higher estimation accuracy. The experiments show that the higher-order origin moments are sensitive to the data, and the lower-order moments should be selected as far as possible. By selecting the appropriate order k, we can obtain ideal estimation parameters. [ABSTRACT FROM AUTHOR]

The traditional power allocation method for multi-target localization adopts the Robust Chance Constrained Power Allocation Scheme (RCC-PA), which does not consider the strong clutter characteristics in the current radar detection environment. However, how to reasonably allocate the power required for the radar to locate the target in the strong clutter is a fundamental challenge for improving the combat capability of the radar. Since Simultaneous Multi-beam Digital Array Radar (SM-DAR) can provide high-resolution information on targets in strong clutter, this paper extends the RCC-PA scheme to strong clutter and introduce an extended target model suitable for high-resolution SM-DAR. At the same time, the Gamma distribution is used to reflect the statistical characteristics of the Radar Cross Section (RCS) so that the extended RCC-PA scheme (ERCC-PA) can be suitable for all the scatterers whose RCS belong to Gamma distribution families. In the ERCC-PA scheme, the Strong Clutter Information Reduction Factor (SCIRF) of the extended target is first derived. Then, a Gamma Chance-constraint Programming Model (Γ -CCP model) is constructed to optimize the power allocation for locating multiple extended targets in strong clutter. The extended dichotomy method for power allocation is also given. Theoretical analysis shows that the power of multi-target localization in strong clutter can still be reasonably allocated under the target with multi-measurement characteristics. In addition, the power distribution of multi-target localization in the strong clutter is negatively correlated with the shape parameter of the Gamma distribution. Specifically, the larger the shape parameter characterizes the larger individual strong scatterer, resulting in the SM-DAR requiring less positioning power. The experimental results verify the theoretical analysis and show that the ERCC-PA scheme can improve the utilization of power compared with the benchmark and has the advantage of robustness to the fluctuation of the target RCS. [ABSTRACT FROM AUTHOR]

Rotating antenna radar is widely used on the airborne platform to extend the range of the radar detection airspace. However, the echo signal received by an airborne rotating radar is spread in the Doppler domain due to the platform motion and the antenna rotation. In the process of a long‐time integration, the antenna rotation harms the signal‐to‐noise ratio and reduces the performance of target detection when using conventional processing methods. Although conventional space‐time adaptive processing (STAP) has been used successfully to suppress clutter on an airborne radar, it does not take into account the antenna rotation. It decreases the performance of clutter suppression on the airborne rotating antenna radar. The signal model of airborne rotating antenna radar is analysed and the STAP technique to the rotating antenna scenario is improved to effectively suppress clutter and focus on target energy. Firstly, the echo of the airborne rotating antenna radar is modelled. Secondly, the factors affecting the Doppler difference of the array elements are investigated. Thirdly, an improved STAP method is proposed. Finally, the effect of antenna rotation on clutter Doppler characteristics and the clutter suppression and target energy focussing on the effects of the improved STAP technique are investigated. Experimental results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, attention is devoted to the analysis of the detection threshold based on the multi‐frame integration in heavy‐tailed clutter for the radar with high resolution and even smaller grazing angle. The closed‐form expressions of both the probability of the detection and the probability of false alarm for the heavy‐tailed clutter background, which can be used for the theoretical analysis of constant false alarm rate (CFAR) detectors, are derived with the multi‐frame integration technique. Accordingly, an improved CFAR detector is designed to work well with the presence of target‐like outliers in the heavy‐tailed clutter. In addition, the proposed CFAR detector is capable to alleviate the masking‐effect resorting to the additive feedback operation when a target is large enough to cross several cells in multi‐target case. The theoretical analysis and numerical simulations demonstrate that the proposed CFAR detector based on multi‐frame integration can improve the signal‐to‐clutter rate of the targets exhibiting better performance than ones based on single frame in heavy‐tailed clutter background. It is validated from the simulations that the proposed CFAR detector with additive feedback operation can deal with masking‐effect for large target occupying several cells. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Due to the advantages of wide coverage, continuous remote monitoring, and high measurement accuracy, the spaceborne multi‐channel surveillance radar has been widely used for the air moving target detection and tracking. In this paper, the minimum detection velocity (MDV) definition for the aerial target detection performance evaluation in a spaceborne multi‐channel radar system is analysed, where three kinds of MDV definitions based on empirical formula, output signal‐to‐clutter‐plus‐noise ratio (SCNR) loss criterion, and output SCNR criterion are discussed in detail based on theoretical analysis and simulation verification. The analysis results will provide valuable reference for the practical spaceborne radar system designment with the air target detection mode. [ABSTRACT FROM AUTHOR]

This letter proposes a data‐driven method to simulate random sea clutter amplitude sequence with distribution and correlation characteristics directly inherited from the measured clutter. First, a generation model is developed to simulate clutter amplitude sequence with desired distribution. By multiplying the measured amplitudes with random disturbance coefficients, the model introduces sample diversity between the simulated and measured sequences. Meanwhile, to quantitatively control the difference of the distribution tails between the simulated and measured sequences (i.e. to ensure their distributions are similar), the threshold error metric is introduced to set the disturbance variance. Second, a scheme combining the generation model with the correlation transfer technique is presented, which allows to simulate clutter amplitude sequence with both desired distribution and correlation for engineering purpose. Experiments based on different measured clutter sets indicate the proposed method works very well. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient clutter cancellation algorithm based on suppressed clutter map for frequency-modulated continuous wave radar systems. In conventional clutter cancellation algorithms based on clutter maps, range-Doppler maps are employed. However, if the radar cross section (RCS) of the targets is significantly lower than the RCS of the clutter, a problem may occur in that the targets may also be removed in the clutter-removal process. To solve this problem, the proposed algorithm creates a range-Doppler map to which the moving target indicator (MTI) algorithm is applied instead of the general range-Doppler map that does not include the MTI algorithm. The range-Doppler map, used after applying the clutter suppression filter, significantly reduces the clutter removal effect on targets in which RCS is relatively weak by suppressing the effect of relatively dominant clutter signals. The experimental results showed that the proposed algorithm detects weak targets that remain undetected by the existing map-based clutter-removal algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a cooperated range ambiguous clutter suppression method for moving target detection in the background of range-ambiguous clutter with a phased array (PA)–frequency diverse array (FDA) dual-mode radar. With the FDA mode, the range-ambiguous clutters are discriminated in the transmit spatial frequency domain, and thus the clutter covariance matrixes (CCMs) corresponding to unambiguous and ambiguous regions can be independently estimated. Therefore, the enhanced CCM can be reconstructed by using a linear combination of these distinguished CCMs from different range regions. With the PA mode, the enhanced CCM is applied, thus taking advantages of its high beampattern gain as well as alleviating the range ambiguous clutter suppression problem. Simulation results are presented to verify the effectiveness of the proposed method in range-ambiguous clutter scenarios. [ABSTRACT FROM AUTHOR]

We demonstrate in this work how we can take advantage of known unfocused SAR (UF-SAR) retracking methods (e.g. the physical SAMOSA model) for retracking of fully-focused SAR (FF-SAR) waveforms. Our insights are an important step towards consistent observations of sea surface height, significant wave height and backscatter coefficient (wind speed) with both UF-SAR and FF-SAR. This is of particular interest for SAR altimetry in the coastal zone, since coastal clutter may be filtered out more efficiently in the high-resolution FF-SAR waveform data, which has the potential to improve data quality. We implemented a multi-mission FF-SAR altimetry processor for Sentinel-3 (S3) and Sentinel-6 Michael Freilich (S6), using a back-projection algorithm, and analysed ocean waveform statistics compared to multilooked UF-SAR. We find for Sentinel-3 that the averaged power waveforms of UF-SAR and FF-SAR over ocean are virtually identical, while for Sentinel-6 the FF-SAR power waveforms better resemble the UF-SAR zero-Doppler beam. We can explain and model the similarities and differences in the data via theoretical considerations of the waveform integrals. These findings suggest to use the existing UF-SAR SAMOSA model for retracking S3 FF-SAR waveforms but the SAMOSA zero-Doppler beam model for S6 FF-SAR waveforms, instead. Testing the outlined approach over short track segments, we obtain range biases between UF-SAR and FF-SAR lower than 2 mm and significant wave height biases lower than 5 cm. [ABSTRACT FROM AUTHOR]

High-precision detection of small targets with low signal-to-noise ratio in infrared (IR) images has great significance for IR precise guidance, video surveillance, etc. In this paper, we proposed a novel method called centre-surround high-boost filter-based improved local Weber contrast measure. First, centre-surround high-boost filter is proposed to fully utilize the grey difference information, which reduces the influence of the non-homogeneous background such as cumulus sky, sea-sky clutters and high brightness edge. Then, an improved local Weber contrast measure is used to construct the contrast operator of the ratio-difference joint form for further target contrast enhancement and arbitrary interference suppression. Finally, we apply an adaptive thresholding segmentation to find the true target. Experimental results on various cluttered background sequences demonstrate that our proposed method can detect small targets with higher detection rate and lower false alarm rate, and the performance is more stable than five comparable methods in diverse scenarios. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for clutter removal in airborne radars using a genetic algorithm and mathematical morphology. The clutter returns are detected when constant alarm rate processing is applied on range-Doppler images. In the proposed method, mathematical morphological operations are performed on range-Doppler images to obtain clutter images. The clutter image is then applied as a mask to remove false detections due to clutter. Also, the targets embedded in clutter are detected using gray-scale morphological operations. The morphological filter and the sequence of operations are designed by a genetic algorithm. The advantage of the proposed method is that it does not require the computation of statistical measures from clutter data and filters are optimally designed using a genetic algorithm. The proposed method has shown an increase in clutter leak reduction when compared to that of a deep morphological network. [ABSTRACT FROM AUTHOR]

In this study, multiple data sets from Beidou geosynchronous Earth orbit (GEO) satellite, ionosonde, magnetometer, Swarm satellite, and meteor radar were used to investigate an anomalous disturbance of the ionosphere in East Asia on 1 November 2016. During 03:00–08:00 universal time (UT), the total electron content (TEC) observed by the Beidou GEO satellite decreased by up to 8.17% at low latitudes and increased by up to 140.19% at middle to low latitudes in East Asia compared to the average TEC on the reference days. The F2 peak electron density (NmF2) measured by ionosondes decreased by 16.00% at Sanya (109.4°E, 18.3°N) and increased by 51.96% and 53.97% at Wuhan (114.4°E, 30.5°N) and Shaoyang (111.3°E, 27.1°N), respectively. The equatorial electric field (EEF) observed by the Swarm satellite at 06:59 UT on 1 November was approximately 77.57% higher than on 4 November. The variations of equatorial electrojet near 100°E and 120°E on 1 November presented an east‐west asymmetry in which there was a significant enhancement in the east while there was no obvious increase in the west. The TEC enhancement derived by the Center for Orbit Determination in Europe also showed the same east‐west asymmetry in the two longitudes. Based on the comprehensive analysis of multiple observational data, the increase in EEF caused the ionospheric TEC and NmF2 to decrease at low latitudes and increase at middle to low latitudes in East Asia during 03:00–08:00 UT on 1 November 2016. The lower atmospheric tidal forcing could contribute to the enhancement of the EEF. Key Points: An anomalous disturbance of ionospheric total electron content (TEC) and NmF2 occurred in East Asia during the geomagnetic quiet day on 1 November 2016The enhancements in the equatorial electrojet and CODE‐TEC near 100°E and 120°E present an east‐west asymmetryThe equatorial electric field enhancement causes the ionospheric TEC to decrease at low latitudes and increase at middle to low latitudes [ABSTRACT FROM AUTHOR]