Radar Method Research Articles

Theory of classical and modified spaceborne synthetic aperture radar imaging

This paper presents a novel method for spaceborne synthetic aperture radar (SAR) imaging, achieved through the statistical solution of the inverse optimization problem of image reconstruction, specifically focusing on radar cross-section (RCS). The proposed method improves upon traditional techniques by incorporating signal decorrelation, implemented via inverse filtering. A comparative analysis is provided between the modified method and the classical aperture synthesis approach, both of which are grounded in the optimization problem solution within the theory of the maximum likelihood method.

A radar emitter individual identification method based on maximum entropy spectrum estimation

A radar emitter individual identification method based on maximum entropy spectrum estimation

Cascade Clutter Suppression Method for Airborne Frequency Diversity Array Radar Based on Elevation Oblique Subspace Projection and Azimuth-Doppler Space-Time Adaptive Processing

Cascade Clutter Suppression Method for Airborne Frequency Diversity Array Radar Based on Elevation Oblique Subspace Projection and Azimuth-Doppler Space-Time Adaptive Processing

A Novel Waveform Optimization Method for Orthogonal-Frequency Multiple-Input Multiple-Output Radar Based on Dual-Channel Neural Networks.

The orthogonal frequency-division multiplexing (OFDM) mode with a linear frequency modulation (LFM) signal as the baseband waveform has been widely studied and applied in multiple-input multiple-output (MIMO) radar systems. However, its high sidelobe levels after pulse compression affect the target detection of radar systems. For this paper, theoretical analysis was performed, to investigate the causes of high sidelobe levels in OFDM-LFM waveforms, and a novel waveform optimization design method based on deep neural networks is proposed. This method utilizes the classic ResNeXt network to construct dual-channel neural networks, and a new loss function is employed to design the phase and bandwidth of the OFDM-LFM waveforms. Meanwhile, the optimization factor is exploited, to address the optimization problem of the peak sidelobe levels (PSLs) and integral sidelobe levels (ISLs). Our numerical results verified the correctness of the theoretical analysis and the effectiveness of the proposed method. The designed OFDM-LFM waveforms exhibited outstanding performance in pulse compression and improved the detection performance of the radar.

Rainfall and water level fluctuations dominated the landslide deformation at Baihetan Reservoir, China

Understanding the deformation patterns of different types of landslides around reservoirs is crucial for precise prevention and control of related hazards. However, under the complex coupling effect of extreme rainfall and reservoir level fluctuations, the response between various types of landslides and triggering factors is still unclear. Our study employed the Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) method and wavelet analysis to undertake an early identification and quantitative analysis of deformation patterns of unstable slopes in the Baihetan Reservoir region, China, and explored the responses of different types of landslide deformation to rainfall and reservoir water level fluctuations. The findings reveal distinct differences in the deformation characteristics of unstable slopes around the reservoir. The quantitative analysis of normalized displacement indicated that the convergent-type landslide exhibits an initial rapid movement that gradually tends to be stable, following an exponential function. The steady-type landslide moves at a consistent speed along a linear trendline. The exponential-type landslide starts slowly and then progressively accelerates. The periodic-type landslide displays periodic fluctuations in displacement, which conforms to the Fourier periodic function. Furthermore, comparisons with antecedent rainfall and reservoir water levels reveal that the convergent-type landslide is significantly influenced by water level fluctuations, and both steady-type and exponential-type landslides are subject to the dual impact of rainfall and water level fluctuations, while the periodic-type landslide exhibits a notable response to rainfall. Therefore, this research aims to provide a scientific basis for the prevention and mitigation of geohazards in reservoir areas.

Радиолокационный метод оценивания углового положения летательного аппарата на основе использования векторных свойств зондирующих сигналов

The radar monopulse method of determining the bearing and roll angles of an aircraft using a passive radio beacon with specified polarization scattering characteristics is considered, in which the reflected radio pulse contains complete information about the navigation elements of the aircraft flight. The passive beacon is two spatially separated in the horizontal plane radar polarization-anisotropic trihedral corner reflectors with horizontal and vertical natural polarizations, respectively. To determine the bearing and roll angles of an aircraft, probing pulse signals are used, the linear plane of polarization of which makes an angle π/4 with the horizontal plane. The bearing of the aircraft is estimated in this plane by a single reflected radio pulse at the output of a two-channel receiving device according to the measured phase difference between the horizontal and vertical polarized components of the received signal. The roll angle is estimated from the measured ratio of the amplitudes of the orthogonally linearly polarized components. Analytical expressions for determining the bearing and roll angles of the aircraft are obtained.

Principles of construction of nanosatellite radar systems based on global navigation satellite system reflectometry

Objectives. The development of radar remote sensing systems based on the reception of signals of navigation satellite systems reflected from the surface enables a constellation of nanosatellites to be deployed, in order to perform radar surveying of the Earth’s surface. The aim of this work is to develop the principles of construction of onboard bistatic remote sensing systems on nanosatellites, in order to assess the energy potential and possibilities for its increase.Methods. The optimal processing method in onboard bistatic radar systems is a development of known analytical methods of optimal processing in monostatic systems. The calculation of the energy potential is based on the experimental data obtained by other authors.Results. The utilization of signals from navigation satellite systems for surface sensing is a promising and developing area. The USA and China have deployed satellite constellations to perform remote sensing using reflected signals of navigation satellites. An algorithm for optimal processing in such systems, which realizes the principle of aperture synthesis, was developed, and the energy potential of bistatic synthetic aperture radar was calculated. In order to achieve this processing, the proposed scheme uses a standard navigation receiver to form reference signals.Conclusions. The application of optimal processing methods in bistatic radar enables a synthetic aperture based on scattered satellite navigation system signals. In order to improve the accuracy of estimates, the signal-to-noise ratio needs to be increased by combining coherent accumulation (aperture synthesis) and incoherent accumulation (aggregating measurements from different spacecraft). The signal processing methods and receiver structure proposed in this work onboard nanosatellites allow aperture synthesis to be achieved with realizable hardware requirements.

Knowledge, Attitudes, and Beliefs Regarding Medical Cannabis Among Patients and Providers in Florida's Long-Term Care Facilities.

Medical cannabis (MC) has been found effective in treating multiple symptoms commonly experienced by older adults; however, residents in long-term care (LTC) often lack access to MC products. This study seeks to identify patterns and barriers to recommending MC to patients and to explore the knowledge and attitudes toward MC use among patients and providers. The quantitative portion of this study employed a survey to assess the knowledge of, attitudes toward, and barriers to MC among 126 providers in Florida LTC. Frequencies were reported, bivariate associations were analyzed, and a final regression model predicting MC knowledge was tested. In-depth interviews were conducted with 25 LTC patients, and content was analyzed using the RADaR method. The ageof the providersranged from 21 to 74; 74% were female, 18% were Black/African American, and 17% reported Hispanic ethnicity. Less than half (37.2%) felt they received adequate training on MC. Having accurate knowledge about MC was associated with greater confidence in answering patients' questions (p=0.002). Although most providers (94.2%) felt MC is a viable treatment option, the main barriers to recommending it to patients were a lack of proper training or clinical guidelines. Regarding patients, 16% reported ever using MC, and less than half (32%) had knowledge of MC or how to obtain products. Many believed it could help with symptoms and would consider its use if recommended by a doctor. However, they reported that MC was rarely recommended by providers and that they knew little about the use of this therapy. This study underscores access challenges among seniors in LTC who might benefit most from MC's therapeutic properties. Complex MC policy implementation issues are discussed. State and federal policy issues around cannabis contribute to limited research on the therapeutic uses of cannabis, as well as the MC access problem addressed in this study.

Short-Term Precipitation Radar Echo Extrapolation Method Based on the MS-DD3D-RSTN Network and STLoss Function.

Short-term precipitation forecasting is essential for agriculture, transportation, urban management, and tourism. The radar echo extrapolation method is widely used in precipitation forecasting. To address issues like forecast degradation, insufficient capture of spatiotemporal dependencies, and low accuracy in radar echo extrapolation, we propose a new model: MS-DD3D-RSTN. This model employs spatiotemporal convolutional blocks (STCBs) as spatiotemporal feature extractors and uses the spatial-temporal loss (STLoss) function to learn intra-frame and inter-frame changes for end-to-end training, thereby capturing the spatiotemporal dependencies in radar echo signals. Experiments on the Sichuan dataset and the HKO-7 dataset show that the proposed model outperforms advanced models in terms of CSI and POD evaluation metrics. For 2 h forecasts with 20 dBZ and 30 dBZ reflectivity thresholds, the CSI metrics reached 0.538, 0.386, 0.485, and 0.198, respectively, representing the best levels among existing methods. The experiments demonstrate that the MS-DD3D-RSTN model enhances the ability to capture spatiotemporal dependencies, mitigates forecast degradation, and further improves radar echo prediction performance.

Sparse decoupling imaging network for wideband two‐dimensional MIMO radar imaging using model‐assisted and attention mechanism

AbstractThe problem of sparse decoupling radar imaging methods based on deep learning is researched. An improved model‐driven learning imaging network with a complex‐valued convolution block attention module plugged into each sub‐network is proposed. This method can solve the high sidelobe and coupling problem in sparse wideband Multiple‐Input Multiple‐Output (MIMO) radar. In addition, it can better focus on the target area and capture target information to boost model representation power. Experimental results verify the validity of the proposed method.

Noncontact Rotational Speed Measurement with Near-Field Microwave of Open-Ended Waveguide

Rotational speed measurement is important for many applications. Here, a noncontact rotational speed test method based on the detection of the periodically perturbed near-field microwave of an open-ended waveguide is proposed. Both simulations and experiments were conducted to verify the near-field microwave rotational speed sensor. The constructed rotation speed sensing system was composed of a standard open-ended WR-42 waveguide (in our measurements, a waveguide-to-coaxial adapter was used to represent an open-ended waveguide) working at ~18 GHz, a radio frequency (RF) circulator, a signal generator, a, RF detector and an oscilloscope. A rotating fan to be measured was placed close to the waveguide’s mouth and, thus, the waveguide’s reflection coefficient was periodically modulated by the rotating fan blades. Then, the RF detector converted this varying reflection coefficient into a direct current (DC) voltage, namely, a periodical waveform. Finally, the rotational speed of the fan could be extracted from this waveform. Measurements using both the proposed near-field microwave method and conventional optical transmission/reflection methods were conducted for verification. The effect of the rotating fan’s location relative to the waveguide’s mouth was also studied. The results show the following: 1. The proposed method works well with a rotational speed of up to ~5000 RPM (rounds per minute), and an accuracy of 1.7% can be achieved. 2. Metallic or non-metallic fan blades are all suitable for this method. Compared with the existing radar method, the proposed method may be advantageous for rotation detection in a constrained space.

Accurate gain method for ground-penetrating radar signals based on stationary wavelet packet transform

In this study, we propose an accurate gain method for ground-penetrating radar (GPR) signals based on the characteristics of refined time-frequency analysis and translation invariance offered by the Stationary Wavelet Packet Transform (SWPT), combined with the conventional signal gain approach. This method aims to address the issue of low signal resolution resulting from the direct gain processing of GPR signals with a low signal-to-noise ratio (SNR). Specifically, the GPR signals are initially decomposed into appropriate wavelet packet coefficients using SWPT, wherein only those coefficients with high SNR undergo gain processing, followed by reconstruction of the signals through SWPT. By employing accurate gain processing on low SNR GPR signals acquired during concrete crack detection tests, we have confirmed that the proposed method effectively distinguishes the target reflected signals from most noise, thereby achieving accurate amplification of the desired reflected signals and significantly enhancing the GPR signals resolution under low SNR conditions.

Polynomial Fitting-Based Noise Reduction for Correlation Functions in Medium-Frequency Radar

In the theoretical calculation of atmospheric wind fields using the cross-correlation analysis method of Medium-Frequency radar, it is necessary to compute a series of correlation parameters from the received echo signals, such as autocorrelation and cross-correlation functions, within the main lobe range of the antenna array to retrieve atmospheric parameters. However, both theoretical analysis and practical applications have shown that the shape of correlation functions can be affected by atmospheric conditions and receiver noise, leading to significant biases in the estimated correlation parameters within the main lobe range. In this study, we theoretically analyze the influence of noise on the amplitude of autocorrelation and cross-correlation functions. We propose a noise reduction method based on the characteristics of correlation functions at the zero-delay point to calculate the noise factor and process the correlation functions within the main lobe range. Furthermore, we conduct simulation analysis to evaluate the performance of this noise reduction method and summarize the effects of the number of fitting points and fitting methods on the noise reduction performance.

Time Series Prediction of Reservoir Bank Slope Deformation Based on Informer and InSAR: A Case Study of Dawanzi Landslide in the Baihetan Reservoir Area, China

Reservoir impoundment significantly impacts the hydrogeological conditions of reservoir bank slopes, and bank slope deformation or destruction occurs frequently under cyclic impoundment conditions. Ground deformation prediction is crucial to the early warning system for slow-moving landslides. Deep learning methods have developed rapidly in recent years, but only a few studies are on combining deep learning and landslide warning. This paper proposes a slow-moving landslide displacement prediction method based on the Informer deep learning model. Firstly, the Sentinel-1 (S1) data are processed to obtain the cumulative displacement time-series image of the bank slope by the Small-BAseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) method. Then, combining data on rainfall, humidity, and horizontal and vertical distances of pixel points from the water table line, this study created a dataset with landslide displacement as the target feature. After that, this paper improves the Informer model to make it applicable to our dataset. This study chose the Dawanzi landslide in the Baihetan reservoir area, China, for validation. After training with 50-time series deformation data points, the model can predict the displacement results of 12-time series deformation data points using 12-time series multi-feature data, and compared with the monitoring values, its Mean Square Error (MSE) was 11.614. The results show that the multivariate dataset is better than the deformation univariate data in predicting the displacement in the large deformation zone of bank slopes, and our model has better complexity and prediction performance than other deep learning models. The prediction results show that among zones I–IV, where the Dawanzi Tunnel is located, significant deformation with the maximum deformation rate detected exceeding –100mm/year occurs in Zones I and III. In these two zones, the initiation of deformation relates to the drop in water level after water storage, with the deformation rate of Zone III exhibiting a stronger correlation with the change in water level. It is expected that deformation in Zone III will either remain slow or stop, while deformation in Zone I will continue at the same or a decreased rate. Our proposed method for slow-moving landslide displacement forecasting offers fast, intuitive, and economically feasible advantages. It can provide a feasible research idea for future deep learning and landslide warning research.

Radar Emitter Recognition Based on Spiking Neural Networks

Efficient and effective radar emitter recognition is critical for electronic support measurement (ESM) systems. However, in complex electromagnetic environments, intercepted pulse trains generally contain substantial data noise, including spurious and missing pulses. Currently, radar emitter recognition methods utilizing traditional artificial neural networks (ANNs) like CNNs and RNNs are susceptible to data noise and require intensive computations, posing challenges to meeting the performance demands of modern ESM systems. Spiking neural networks (SNNs) exhibit stronger representational capabilities compared to traditional ANNs due to the temporal dynamics of spiking neurons and richer information encoded in precise spike timing. Furthermore, SNNs achieve higher computational efficiency by performing event-driven sparse addition calculations. In this paper, a lightweight spiking neural network is proposed by combining direct coding, leaky integrate-and-fire (LIF) neurons, and surrogate gradients to recognize radar emitters. Additionally, an improved SNN for radar emitter recognition is proposed, leveraging the local timing structure of pulses to enhance adaptability to data noise. Simulation results demonstrate the superior performance of the proposed method over existing methods.

Broadband stepped-frequency radar waveform generation by Fourier domain mode-locking period-one laser dynamics.

A stepped-frequency (SF) radar waveform generation method based on Fourier domain mode-locking (FDML) period-one laser dynamics is proposed and demonstrated. By fast controlling the optical injection strength of a semiconductor laser through electro-optical modulation, a broadband SF signal is generated. By further introducing an optoelectronic feedback loop with its round trip time delay matched with the temporal period of the SF signal, FDML is enabled through which the frequency stability, accuracy, and in-band signal-to-noise ratio are greatly improved. In the experiment, SF signals with a bandwidth of 6 GHz (12-18 GHz) and a frequency step of 150 MHz are generated. By comparing the qualities of signals generated with and without FDML, advantages of the proposed SF signal generation method are verified. Based on the proposed SF signal generation method, high-resolution inverse synthetic aperture radar (ISAR) imaging is also demonstrated, in which the 2D imaging resolution reaches 2.6 cm × 3.82 cm.

Comparability of radar and optical methods in identifying surface water in a semi‐arid protected area

AbstractSurface water assumes a pivotal role in sustaining a wide range of wildlife species in semi‐arid protected areas. However, differences in surface water body typology, underlying soil type, wildlife activity, the presence of phytoplankton amongst other factors, result in high variability of surface water spectral reflectance and detection accuracy. In this study, the performance of radar and optical methods was evaluated in detecting surface water of variable spectral reflectance in Hwange National Park, Zimbabwe using Sentinel‐1 radar and Sentinel‐2 optical images for the period 2016–2023. Results demonstrated that radar methods had low surface water detection accuracy which was highly variable as shown by overall accuracy and kappa statistic measures which continuously changed over time compared with optical methods. The overall best‐performing method was the optical AWEInsh (sharpened) which showed high surface water detection accuracy and consistency (OA: 94%–100%) and (κ: 0.88–1.00) from 2016 to 2023. Therefore, optical methods present a stable and robust way for surface water monitoring in heterogeneous semi‐arid protected areas. However, radar‐based methods should be continually explored where optical‐based technologies are impeded as a result of vegetation cover and cloud conditions.

Method for remote measurement of specific conductivity and moisture of subsurface soil horizons

The aim of the research is to develop a radar method for determining the physical and chemical parameters of subsurface soil horizons, providing rapid determination of moisture and specific conductivity in the area of the plant root system. The proposed method is based on a set of Fresnel equations which describe reflection of electromagnetic waves from the interface between dielectric media in vertical and horizontal polarization of the probing signal. For the practical implementation of the method, it is proposed to use two unmanned aerial vehicles that form a bistatic radar system which irradiates the Earth's surface obliquely in order to create the Brewster's effect and increase the fraction of the radio signal reflected from subsurface horizons. The percentage of moisture and the specific conductivity of soil are calculated from the measured values of the imaginary part of the complex permittivity. The required accuracy of moisture and conductivity measurements is achieved by two-step calibration of the measuring device. The values of the moisture content and specific conductivity of soil obtained by radar at a frequency of 469 MHz are in good agreement with the results of measuring these parameters using the soil moisture meter TDR 150 Spectrum Technologies, Inc.

Sub-Aperture Synthetic Aperture Radar Imaging of Fixed-Platform Beam-Steering Radar for Blast Furnace Burden Surface Detection.

Due to the scheme of fixed-platform beam-steering radar and the space of the blast furnace being subjected to harsh environmental influences, the traditional detection methods of burden surface are limited by geometric distortion, noncoherent clutter, and noise interference, which leads to an increase in the image entropy value and the equivalent number of views, makes the density distribution of burden surface show a diffuse state, and greatly affects the stability and accuracy. In this paper, a new fixed-platform beam-steering radar synthetic aperture radar imaging method (FPBS-SAR) is proposed in the sensory domain of the blast furnace environment. From the perspective of fixed-platform beam-steering radar motion characteristics, the target range-azimuth coupled distance history model under the sub-aperture is established, the azimuthal Doppler variation characteristics of the fixed-platform beam-steering process are analyzed, and the compensation function of the transform domain for geometric disturbance correction is proposed. For noncoherent noise suppression in blast furnaces, the trimmed geometric mean-order-likelihood CFAR method is proposed to take into account the information of burden surface and clutter suppression. To verify the method, point target simulation and imaging for the industrial field measurement data are carried out. The results indicate that geometric distortion is well eliminated, the image entropy value and the equivalent number of views have decreased, and noncoherent noise in blast furnaces is suppressed.

Method for extracting temperature distribution characteristics of municipal asphalt pavement construction in high-temperature weather under compaction quality constraint

ABSTRACT In order to improve the temperature detection ability of municipal asphalt pavement in high-temperature weather, a method to extract the temperature distribution characteristics of municipal asphalt pavement in high-temperature weather under the constraint of compaction quality was proposed. Using a digital image system, laser profiler, and ground penetrating radar system, collect construction temperature data of urban asphalt pavement under the constraints of compaction quality under high-temperature weather conditions, and use time series analysis to reconstruct the data. Combined with the nondestructive detection method of ground penetrating radar, the echo analysis model of temperature information is established, and the characteristics of temperature distribution are reconstructed using the spectral analysis method. The time–frequency transformation method is used to transform the time series of construction temperature under the constraint of compaction quality. The discrete signal window method was used to analyse the temperature characteristics of construction. According to the characteristic state monitoring, the temperature distribution characteristics are extracted. The test results show that the method is reliable in extracting the temperature distribution characteristics of municipal asphalt pavement construction under high-temperature weather and improves the real-time monitoring capability of asphalt pavement temperature.