Radar Data Processing Research Articles

Unwrap-Net: A deep neural network-based InSAR phase unwrapping method assisted by airborne LiDAR data

In Interferometric Synthetic Aperture Radar (InSAR) data processing, accurately unwrapping the phase is crucial for measuring elevation or deformation. DCNN models such as PhaseNet and PGNet have improved the efficiency and accuracy of phase unwrapping, but they still face challenges such as incomplete multiscale feature learning, high feature redundancy, and reliance on unrealistic datasets. These limitations compromise their effectiveness in areas with low coherence and high gradient deformation. This study proposed Unwrap-Net, a novel network model featuring an encoder-decoder structure and enhanced multiscale feature learning via ASPP (Atrous Spatial Pyramid Pooling). Unwrap-Net minimizes feature redundancy and boosts learning efficiency using SERB (Residual Convolutional with SE-block). For dataset construction, airborne LiDAR terrain data combined with land cover data from optical images, using the SGS (Sequential Gaussian Simulation) method, are used to synthesize phase data and simulate decorrelation noise. This approach creates a dataset that closely approximates real-world conditions. Additionally, the introduction of a new high-fidelity optimization loss function significantly enhances the model’s resistance to noise. Experimental results show that compared to the SNAPHU and PhaseNet models, the SSIM of the Unwrap-Net model improves by over 13%, and the RMSE is reduced by more than 34% in simulated data experiments. In real data experiments, SSIM improves by over 6%, and RMSE is reduced by more than 49%. This indicates that the unwrapping results of the Unwrap-Net model are more reliable and have stronger generalization capabilities. The related experimental code and dataset will be made available at https://github.com/yangwangyangzi48/UNWRAPNETV1.git.

Mapping engineering communications based on radiovision and augmented reality technologies

Abstract The article presents a mathematical apparatus of radio wave tomosynthesis for obtaining a radar image using an ultra-wideband multi-channel antenna array. Also presented are the results of processing radar data and the resulting three-dimensional radio tomogram of inhomogeneities in a homogeneous layer. Using the example of restoring the shape of test objects, the possibility of isolating individual layers at the required depth is shown. By combining radio wave and video information, the possibility of implementing augmented reality is shown. This combination provides a more visual interpretation of GPR data in construction and repair tasks.

Comparative analysis of hybrid cognitive radar techniques for enhanced target detection and tracking: A performance evaluation perspective

This paper introduces an innovative hybrid algorithmic approach for cognitive radar systems, by integrating unique machine learning optimization techniques such as, YOLO (You Only Look Once), Mask R-CNN, and Recurrent Neural Networks (RNNs). Through extensive simulations, the integrated approach demonstrates notable enhancements in target detection, instance segmentation, and target tracking within radar systems. Leveraging deep learning models, the framework facilitates adaptive and intelligent processing of radar data, augmenting system performance in dynamic environments. By seamlessly integrating state-of-the-art techniques, the proposed framework showcases a comprehensive solution to the challenges faced by traditional radar systems. This research represents a significant stride forward in radar technology, promising transformative impacts across various domains, including surveillance, remote sensing, and autonomous navigation. As radar systems continue to evolve, the adoption of advanced deep learning techniques offers unprecedented opportunities for enhancing situational awareness and decision-making capabilities in complex operational scenarios.

Optimization of the automotive millimeter-wave radar data processing using the modified MFNN neural network

BACKGROUND: Recognition and representation of the road scene is a relevant task in the field of autonomous driving. One of the ways to improve the characteristics of the existing sensory hardware is the use of neural networks for signal processing. AIM: To conduct an experimental study of the possibilities of using the modified MFNN neural network to increase the resolution of a radar with a small number of channels, to compare it with the classical algorithm based on the fast Fourier transform (FFT), and, in addition, to compare the results with the data obtained from other types of sensors (lidars). METHODS: The algorithm of the road scene representation, in particular, the detection of pedestrians and cars, is used with a millimeter-range automotive radar and a method for determining data components in relation to the DOA problem, based on the MFNN neural network, modified for the case of representing signals taking complex values in the form of excessive basis coefficients minimizing these coefficients according to the L1 norm. The algorithm based on the fast Fourier transform (FFT) is used for comparative analysis. RESULTS: As a result of the conducted research, confirmation of the practical feasibility of the developed modifications of the MFNN method was obtained, and the advantage of using a neural network, consisting in increasing the degree of detail of objects, the accuracy of determining their shape and position using a radar with a small number of channels, was demonstrated. CONCLUSION: The obtained results can be used to develop solutions to improve the efficiency of detecting obstacles on the way of transport, automatic vehicle control, continuous environmental monitoring, and so on, which helps to improve the safety and efficiency of highly automated and autonomous systems.

Interpretation of georadar data based on machine learning technologies

In the laboratory analysis of geological data, a number of problems arise due to the insufficient accuracy of the results. For example, different laboratories may provide different end results for the same samples, which creates a problem. This can lead to unreliable results, which can ultimately reduce the quality of the assessment. Machine learning allows to speed up the processing of radar data, as well as avoid the above-mentioned "misunderstandings". The problem of conducting scientific research at specialized landfills for a comprehensive assessment of the possibilities of using computer technology in the interpretation of georadar profiles is urgent. This makes it possible to objectively evaluate the result of automatic interpretation of georadar data. The several machine-learning algorithms described in the article are designing to improve the analysis and interpretation of data by incorporating various methods for optimizing georadar data processing processes. These methods include regression, classification and clustering. By incorporating these methods of optimizing the processing of georadar data into several machine-learning algorithms, the software can provide a comprehensive analysis and interpretation of the data obtained. This allows for a better understanding of the relationships, patterns and trends in the data, which ultimately leads to more informed decision-making and improved understanding. To improve the understanding of the results, the following quantitative indicators were obtained: correlation coefficient – 0.7072, determination coefficient – 0.5001, all these indicators correspond to these models. The deviation from the regression line is on average 22.37 units. Based on the classification results, the soil was determined to be wet. Errors in the sets do not exceed 1 %

Design and application of a radar target simulator

Abstract To fulfill the need for debugging boundary scenes in radar data processing development, this paper proposes a simulator design method for the editing and simulation of diverse combat scenes as required. The simulator was developed based on the VPX platform, including external control equipment, resource scheduling, beam control, target simulation, and data processing. The motion model is based on linear and circular models, the clutter model algorithm is the zero memory nonlinear transformation method based on K distributions, and the noise model is designed as Gaussian white noise, characterized by a probability distribution in the Gaussian distribution and a constant power spectrum. The generated plots are condensed, and their quality is calculated, for which the rate of plot rejection can reach more than 50% by controlling the feature elements. Finally, the simulated target trajectory is visually verified through Matlab.

Python Indian Weather Radar Toolkit (pyiwr): An open-source Python library for processing, analyzing and visualizing weather radar data

The Python Indian Weather Radar Toolkit, abbreviated as "pyiwr", is an open-source Python library tailored for the purpose of handling data from the Indian Doppler Weather Radar (DWR). This paper provides a comprehensive overview of the pyiwr, which serves as a toolkit to read, analyze, process, and visualize weather radar data. Apart from this, the toolkit offers a range of robust functions implementing various algorithms covering several aspects of the radar data processing and quality control that facilitate the manipulation and analysis of weather radar data. To demonstrate the practical applicability of pyiwr, various case studies are presented, focusing on processing raw reflectivity data (clutter correction), Quantitative Precipitation Estimation (QPE) using Z-R relationship and time-series analysis of reflectivity and rain intensity, both spatially as well as at a specific location, during various meteorological events. This module enhances the accessibility and compatibility of radar data, enabling researchers, weather forecasters, and hydrologists to efficiently work with DWR data (particularly Indian DWR) that fosters advancements in weather radar research and applications. The open availability of pyiwr's source code on GitHub ensures that researchers and practitioners can not only access the toolkit but also contribute to its ongoing development.

Adapting Object Detection Models for Multi-Target Detection Utilizing Radars

This paper investigates the use of Deep Learning (DL) in multiple input multiple output (MIMO) radar target detection, focusing on azimuth and elevation estimation. Traditional methods face challenges like interference and reflections, especially in multi-target scenarios. Feature extraction conventionally relies on range correlation, Doppler filtering, and angle beamforming, followed by detection after constant false alarm rate (CFAR) processing. However, early data sparsification by bin selection often leads to information loss, particularly with large data cubes required for practical implementation. Deep Learning techniques offer an alternative, specifically in azimuth and elevation detection at earlier stages of radar data processing. We developed a convolutional neural network (CNN) model that achieved Mean Square Errors (MSE) of 0.149 for azimuth and 0.168 for elevation on single-target data from 5,000 samples. The model's performance in dual-target scenarios showed MSEs ranging from 0.838 to 1.845, tested on 8,000 samples from a dataset of 72,000. This paper details the model development process, its impact on radar target detection, and potential future research directions involving the substitution of multi-bin Deep Learning blocks with traditional methods.

RPC-EAU: Radar Plot Classification Algorithm Based on Evidence Adaptive Updating

Accurately classifying targets and clutter plots is crucial in radar data processing. It is beneficial for filtering out a large amount of clutters and improving the track initiation speed and tracking accuracy of real targets. However, in practical applications, this problem becomes difficult due to complex electromagnetic environments such as cloud and rain clutter, sea clutter, and strong ground clutter. This has led to poor performance of some commonly used radar plot classification algorithms. In order to solve this problem and further improve classification accuracy, the radar plot classification algorithm based on evidence adaptive updating (RPC-EAU) is proposed in this paper. Firstly, the multi-dimensional recognition features of radar plots used for classification are established. Secondly, the construction and combination of mass functions based on feature sample distribution are designed. Then, a confidence network classifier containing an uncertain class was designed, and an iterative update strategy for it was provided. Finally, several experiments based on synthetic and real radar plots were presented. The results show that RPC-EAU can effectively improve the radar plot classification performance, achieving a classification accuracy of about 0.96 and a clutter removal rate of 0.95. Compared with some traditional radar pattern recognition algorithms, it can improve by 1 to 10 percentage points. The target loss rate of RPC-EAU is also the lowest, only about 0.02, which is about one third to one half of the comparison algorithms. In addition, RPC-EAU avoids clustering all radar points in each update, greatly saving the computational time. The proposed algorithm has the characteristics of high classification accuracy, low target loss rate, and less computational time. Therefore, it is suitable for radar data processing with high timeliness requirements and multiple radar plots.

STAM-LSGRU: a spatiotemporal radar echo extrapolation algorithm with edge computing for short-term forecasting

With the advent of Mobile Edge Computing (MEC), shifting data processing from cloud centers to the network edge presents an advanced computational paradigm for addressing latency-sensitive applications. Specifically, in radar systems, the real-time processing and prediction of radar echo data pose significant challenges in dynamic and resource-constrained environments. MEC, by processing data near its source, not only significantly reduces communication latency and enhances bandwidth utilization but also diminishes the necessity of transmitting large volumes of data to the cloud, which is crucial for improving the timeliness and efficiency of radar data processing. To meet this demand, this paper proposes a model that integrates a spatiotemporal Attention Module (STAM) with a Long Short-Term Memory Gated Recurrent Unit (ST-ConvLSGRU) to enhance the accuracy of radar echo prediction while leveraging the advantages of MEC. STAM, by extending the spatiotemporal receptive field of the prediction units, effectively captures key inter-frame motion information, while optimizations to the convolutional structure and loss function further boost the model’s predictive performance. Experimental results demonstrate that our approach significantly improves the accuracy of short-term weather forecasting in a mobile edge computing environment, showcasing an efficient and practical solution for processing radar echo data under dynamic, resource-limited conditions.

An Improved Synchrosqueezing S-Transform and Its Application in a GPR Detection Task.

The S-transform is a fundamental time-frequency (T-F) domain analysis method in ground penetrating radar (GPR) data processing and can be used for identifying targets, denoising, extracting thin layers, and high-resolution imaging. However, the S-transform spectrum experiences energy leakage near the instantaneous frequency. This phenomenon causes frequency components to erroneously spread over a wider range, impacting the accuracy and precision of GPR data processing. Synchrosqueezing is an effective method to prevent spectrum leakage. In this work, we introduce the synchrosqueezing generalized phase-shifting S-transform (SS-GPST). Initially, it resolves the compatibility issue between the S-transform and the synchrosqueezing strategy through phase-shifting. Subsequently, the SS-GPST accomplishes spectral energy focusing and resolution enhancement via a generalized parameter and synchrosqueezing. A synthetic signal test shows that the SS-GPST excels over other methods at focusing degree, spectral resolution, and signal reconstruction accuracy and speed. In actual GPR tunnel detection data processing, we assess the adaptability of the SS-GPST from three aspects: spectral energy distribution, thin layer identification, and data denoising. The results indicate: (1) compared to other methods, the SS-GPST accurately expresses spectral components with a strong focusing degree and fewer interference components; (2) high-frequency slices of the SS-GPST accurately detect the top and bottom interfaces of a 3.0-3.5 cm reinforcement protection layer; and (3) due to fewer interference components in the SS-GPST spectrum, reconstructing GPR profiles through the SS-GPST inverse transform is an efficient denoising technique. The SS-GPST demonstrates adaptability to different data processing purposes, offers high-resolution T-F spectra, and shows potential to supersede the S-transform.

Real-time quality assurance and quality control for a high frequency radar network

This paper recommends end to end quality assurance methods and quality control tests for High Frequency Radar Networks. We focus on the network that is operated by the Mid Atlantic Regional Association Coastal Ocean Observing System (MARACOOS). The network currently consists of 38 radars making real-time measurements of the surface currents over the continental shelf for a variety of applications including search and rescue planning, oil spill trajectory modelling and providing a transport context for marine biodiversity observing networks. MARACOOS has been delivering surface current measurements to the United States Coast Guard (USCG) since May 2009. Data quality is important for all applications; however, since the USCG uses this surface current information to plan life-saving missions, delivery of the best quality data is crucial. We have mapped the components of the HF radar data processing chain onto the data levels presented in the NASA Earth Science Reference Handbook and have applied quality assurance and quality control techniques at each data level to achieve the highest quality data. There are approximately 400 High Frequency radars (HFRs) deployed globally and the presented techniques can provide a foundation for data quality checks and standardization of the data collected by the large number of systems operating today.

MmDetect: YOLO-Based Processing of mm-Wave Radar Data for Detecting Moving People

mmDetect: YOLO-Based Processing of mm-Wave Radar Data for Detecting Moving People

A Crowd Movement Analysis Method Based on Radar Particle Flow.

Crowd movement analysis (CMA) is a key technology in the field of public safety. This technology provides reference for identifying potential hazards in public places by analyzing crowd aggregation and dispersion behavior. Traditional video processing techniques are susceptible to factors such as environmental lighting and depth of field when analyzing crowd movements, so cannot accurately locate the source of events. Radar, on the other hand, offers all-weather distance and angle measurements, effectively compensating for the shortcomings of video surveillance. This paper proposes a crowd motion analysis method based on radar particle flow (RPF). Firstly, radar particle flow is extracted from adjacent frames of millimeter-wave radar point sets by utilizing the optical flow method. Then, a new concept of micro-source is defined to describe whether any two RPF vectors originated from or reach the same location. Finally, in each local area, the internal micro-sources are counted to form a local diffusion potential, which characterizes the movement state of the crowd. The proposed algorithm is validated in real scenarios. By analyzing and processing radar data on aggregation, dispersion, and normal movements, the algorithm is able to effectively identify these movements with an accuracy rate of no less than 88%.

ПРИНЦИП СУБЪЕКТИВНОЙ ИЗБЫТОЧНОСТИ И МЕТОД СЖАТИЯ ДАННЫХ, ПОЛУЧАЕМЫХ ОТ ЦИФРОВЫХ АНТЕННЫХ РЕШЕТОК

The problem of limiting the capacity of the diagram-forming circuit of the digital antenna array is considered, due to the capabilities of existing communication lines with serial data transmission from the receiving channels to the computer. This problem limits the possibilities of implementing various methods of signal processing in digital antenna arrays. The subject of the study is the possibility of increasing the capacity of the diagram-forming circuit of the digital antenna array due to software compression of radar data. The purpose of the work is to create a scientific and methodological apparatus that will increase the capcity of the diagram-forming scheme by using the principle of subjective redundancy in relation to radar data processing tasks. The results of the work include: justification and formulation of the principle of subjective redundancy of radar data and the method of compression of radar data, as well as the results of numerical studies confirming the operability of the proposed scientific and methodological apparatus. The principle of subjective redundancy is based on the independence of the separation of signal processing processes in the spatial and temporal domains when receiving a single echo signal, and in the case of receiving several echo signals, on the principle of superposition of fields from various sources in the far antenna zone. The method of radar data compression is based on the principle of subjective redundancy of radar data and differs from the known iterative procedure for approximating the matrix of complex samples in the form of a superposition of products of one-dimensional cosine spectra. It is shown that the application of the radar data compression method makes it possible to increase the transmission capacity of the digital antenna array transmission lines several times.

Adaptive Algorithms in Radar Signal Processing

The development of modern technologies has fundamentally transformed the field of radar signal and data processing. With the use of advanced algorithms and computational power, radars are now capable of extracting crucial information from received signals, facilitating improved target identification and tracking. This article presents some of the advanced technologies employed in radar signal and data processing and their impact on adaptability of radar systems. It traces the evolution of radar technology from old systems to the present, emphasizing the benefits of adaptive radar signal processing, which includes algorithms such as adaptive beamforming, Space-Time Adaptive Processing, and the integration of Machine Learning and Artificial Intelligence. In conclusion, challenges, and future prospects in the field of radar systems are discussed, with a focus on the potential integration of Artificial Intelligence methods, Cognitive radars, and Multiple Input Multiple Output technologies. Despite technical obstacles, opportunities emerge to enhance the performance of radar systems and achieve new levels of efficiency.

Advancements in Onboard Processing of Synthetic Aperture Radar (SAR) Data: Enhancing Efficiency and Real-Time Capabilities

Advancements in Onboard Processing of Synthetic Aperture Radar (SAR) Data: Enhancing Efficiency and Real-Time Capabilities

The Evolution of Moore’s Law Through Chipletized Architectures

Staying ahead of America’s adversaries requires control of the electromagnetic spectrum and therefore an evolution of the sensor processing chain. Agile, chip-scale, direct analog-to-digital solutions at the sensor edge are needed to overcome data bandwidth and latency limitations that are inherent with legacy EW and radar systems, and reduce design cycles, size, weight, power, and cost (SWaP-C). Heterogeneous, 2.5D integration is the next phase of Moore’s Law enabling a new approach to microelectronics based on chipletized architectures. The acceleration of deployable modular, high-performance microelectronics to the tactical edge is achieved by integrating the best silicon processing nodes for analog/digitization, processing, memory, and AI functionality on one chip. Mercury is partnering with leading semiconductor providers to deliver to defense customers the only trusted, RF system-in-package (RFSiP) devices that utilize an ecosystem of advanced, die-level building blocks, called chiplets, at the speed of innovation and purpose-built for each mission. Through assembly of high-density chiplets on a silicon interposer, on-chip die-to-die interconnects enable efficient conversion between analog and digital signals, signal processing, high-speed IO, and power management while reducing system complexity and SWaP-C. The use of open standard die-to-die interfaces allows chiplets to be rapidly upgraded or modified to meet the intended use case. In this paper, Mercury explores how 2.5D integration of state-of-the-art chiplets on a silicon interposer solves next-generation EW and radar system data processing challenges.

Removal of Diagonal Strip Noise in Airborne Ice Penetrating Radar Data

Airborne ice-penetrating radar (IPR) is a powerful geophysical method for detecting ice thickness, subglacial topography and internal layers in the vast and frigid Antarctic ice sheet. Data processing is an essential step in improving the signal-to-noise ratio (SNR). In order to eliminate the diagonal strip noise mainly present in airborne IPR data of the Amery ice shelf and Grove Mountain regions, traditional ground penetrating radar processing methods such as F-K (frequency-wavenumber domain) filtering and F-K migration are applied. The validity of the data processing methods is proved by the quantitative index of image quality evaluation. It provides a reference for subsequent airborne radar data processing and application.

Spectral estimation model for linear displacement and vibration monitoring with GBSAR system

In recent years, there has been a growing interest in the development of ground-based Synthetic Aperture Radars (GBSAR) for the purpose of monitoring structural displacements. GBSAR offers high-resolution monitoring over a wide area and can capture data every few minutes. However, compact high-frequency multiple input multiple output (MIMO) radars have emerged as an alternative for monitoring sub-second displacements, such as structural vibrations. MIMO radar has sub-second acquisition interval. However, it has limited cross-range resolution compared to GBSAR, and interference between antennas and presence of multiple scatterers in the scene can cause strong sidelobes in the processed data. On the other hand, GBSAR utilizes a long synthetic aperture to achieve high cross-range resolution. However, due to its longer data acquisition time compared to MIMO radar, conventional methods are insufficient for detecting scatterers’ sub-second displacements that occur during the data acquisition process. This study proposes a method to effectively monitor sub-second or sub-minute displacements using GBSAR signals. The proposed method enhances the conventional radar interferometric processes by employing spectral estimation, allowing for multi-dimensional detection of targets’ azimuth angle, linear displacement, and vibrational characteristics. Consequently, this method improves both the processing of MIMO radar data and enables high-resolution fast displacement monitoring from GBSAR signals. The paper presents the theoretical details and mathematical formulations of the proposed method for both MIMO radar and GBSAR imaging modes. To evaluate the effectiveness of the proposed method, numerical simulations and real experiments are conducted. The experimental results validate the capability of the proposed method in both GBSAR and MIMO configuration modes for high-resolution monitoring of fast linear displacements and vibrations. The results exhibit promising signal-to-noise ratio (SNR) and peak-to-sidelobe ratio (PSLR) values.