range-Doppler Imaging Research Articles

Ray-Tracing-Assisted SAR Image Simulation under Range Doppler Imaging Geometry

In order to achieve an effective balance between SAR image simulation fidelity and efficiency, we proposed a ray-tracing-assisted SAR image simulation method under range doppler (RD) imaging geometry. This method utilizes the spatial traversal mode of RD imaging geometry to transmit discrete electromagnetic (EM) waves into the SAR radiation area and follows the Nyquist sampling law to set the density of transmitted EM waves to effectively identify the beam radiation area. The ray-tracing algorithm is used to obtain the backscatter amplitude and real-time slant range of the transmitted EM wave, which can effectively record the multiple backscattering among the components of the distributed target so that the backscattering subfields of each component can be correlated. According to the RD condition equation, the backscattering amplitude is assigned to the corresponding range gate, and the three-dimensional (3D) target is mapped into the two-dimensional (2D) SAR slant-range coordinate system, and the SAR target simulated image is directly obtained. Finally, the simulation images of the proposed method are compared qualitatively and quantitatively with those obtained by commercial simulation software, and the effectiveness of the proposed method is verified.

Convolutional Neural Network-Based Drone Detection and Classification Using Overlaid Frequency-Modulated Continuous-Wave (FMCW) Range-Doppler Images.

This paper proposes a novel drone detection method based on a convolutional neural network (CNN) utilizing range-Doppler map images from a frequency-modulated continuous-wave (FMCW) radar. The existing drone detection and identification techniques, which rely on the micro-Doppler signature (MDS), face challenges when a drone is small or located far away, leading to performance degradation due to signal attenuation and faint (MDS). In order to address these issues, this paper suggests a method where multiple time-series range-Doppler images from an FMCW radar are overlaid onto a single image and fed to a CNN. The experimental results, using actual data for three different drone sizes, show significant performance improvements in drone detection accuracy compared to conventional methods.

Range-Doppler image classification for interference detection in over-the-horizon radar

Sky-wave over-the-horizon radar (OTHR), as a useful tool for beyond-line-of-sight target detection, is inevitably affected by radio frequency interference (RFI) and transient interference (TSI). Conventional interference detection is usually based on radar signal processing. This paper proposes to detect the interference by classifying the range-Doppler (RD) image into three categories, i.e. RFI, TSI, and NoI (No Interference), according to the texture differences. Unlike the conventional way, RD image classification has direct reflection of the interference's influence on target detection. To design the RD image classifier, we develop signal models to construct the simulated database, use real data to construct the real database, and employ various classification theories, e.g. deep learning, traditional machine learning, feature fusion, and ensemble learning. Experimental results show that the RD image classifier is an effective and promising tool for interference detection. Strong interference can be accurately detected, while weak interference needs more study in the future.

Adaptive carrier-phase-noise-canceled LiDAR for range-Doppler imaging beyond hundreds of laser coherence length.

Carrier-phase noise limits both the performance and the maximum operation range of coherent LiDAR. To address this issue, we propose a carrier-phase-noise-canceled LiDAR based on an auxiliary interferometer and adaptive filters. Compared to previous methods, this approach is calibration-free and offers higher compensation accuracy, as well as applicability of dynamic target detection. Experiments of range-Doppler imaging for stationary targets and rotating extended targets have been performed, and the detection results close to the theoretical resolution were obtained at the round trip distance to the target beyond 981 times and 106 times coherence length, respectively.

Enhancing Radar Image Classification with Autoencoder-CNN Hybrid System

The tracking, analysis, and classification of human movements can be crucial, particularly in areas such as elderly care, healthcare, and infant care. Typically, such tracking is done remotely with cameras. However, radar systems have emerged as significant methods and tools for these tasks due to their advantages such as privacy, wireless operation, and the ability to work through walls. By converting reflected radar signals from targets into images, human activities can be classified using powerful classification tools like deep learning. In this study, range-Doppler images of behind-the-wall human movements obtained with a radar system consisting of one transmitter and four receiver antennas were classified. Since the data collected from the four receiver antennas are in different positions, the collected reflection signals also differ. The signals collected with the range-time matrix content were divided into positive and negative parts, resulting in eight images from the four antennas. Instead of using all the data in CNN training, the images were first subjected to a reconstruction process with an autoencoder to reduce differences. As a result, it was observed that reconstructing the images with an autoencoder before classification with CNN increased the classification success. In conclusion, it was observed that the classification success of radar images can be increased by using a hybrid system with an autoencoder to reconstruct the images before classification with CNN.

Bounding Volume Hierarchy-Assisted Fast SAR Image Simulation Based on Spatial Segmentation

In order to improve the simulation efficiency under the premise of ensuring the fidelity of synthetic aperture radar (SAR) simulation images, we propose a BVH-assisted fast SAR image simulation method based on spatial segmentation. The beam scanning model is established based on RD imaging geometric relation, and the bounding volume hierarchy (BVH) algorithm is used to assist in obtaining the time-varying latticed radiation and shadow areas within the radar beam, combining them with the real-time position of the sensors to complete the simulation of the electromagnetic (EM) wave transmission. The ray tracing algorithm is used to calculate the multiple backscatter fields of EM waves, including various material properties of the target surface. The SAR spatial traversal is adopted to spatially segment the latticed radiation area, and the compute unified device architecture (CUDA) kernel function is designed using the echo matrix cell method to make each cell of the target echo matrix as a subfield of the backscattering field, and the position of the echo matrix cell is traversed to obtain the target backscattering field. The target simulated echo is processed by the range Doppler (RD) imaging algorithm to obtain the SAR-simulated image. The simulation results show that compared with a CPU single-thread simulation, the simulation speed of the proposed method is significantly improved, and the SAR simulation image has high structural similarity with the real image, which fully verifies the effectiveness of the proposed method.

Improving Deep CNN-Based Radar Target Classification Performance by Applying a Denoise Filter

This paper presents a novel method for removing noise from range-Doppler images by using a filter prior to conducting target classification using a deep neural network. Specifically, Kuan, Frost, and Lee filters are employed to eliminate speckle noise components from radar data images. Furthermore, a neural network that combines residual and inception blocks (RINet) is proposed. The RINet model is trained and tested on the RAD-DAR dataset—a collection of range-Doppler feature maps. The analysis results show that the application of a Lee filter with a window size of 7 in the RAD-DAR dataset demonstrates the most improvement in the model’s classification performance. On applying this noise filter to the dataset, the RINet model successfully classified radar targets, exhibiting a 4.51% increase in accuracy and a 14.07% decrease in loss compared to the classification results achieved for the original data. Furthermore, a comparison of the RINet model with the noise filtering solution with five other networks was conducted, the results of which show that the proposed model significantly outperforms the others.

Classification of human target movements behind walls using multi-channel range-doppler images

Classification of human target movements behind walls using multi-channel range-doppler images

A Fall Detection System Based on FMCW Radar Range-Doppler Image and Bi-LSTM Deep Learning

A Fall Detection System Based on FMCW Radar Range-Doppler Image and Bi-LSTM Deep Learning

Fast SAR Image Simulation Based on Echo Matrix Cell Algorithm Including Multiple Scattering

We present a novel fast synthetic aperture radar (SAR) image simulation method based on the echo matrix cell algorithm including multiple scattering. To improve the efficiency of SAR image simulation while ensuring the fidelity of the simulated results, we first discretized the target facets set in the SAR beams footprint into lattice targets using the range-Doppler (RD) imaging geometry model and provided the basis for simulating electromagnetic wave transmission. Based on the simulation of electromagnetic waves transmission, we used the ray tracing algorithm to calculate the multiple backscattering field including multi-polarimetric information and various material properties. Then, based on the echo matrix cell algorithm, we set the echo matrix cell as the subfield of the target backscattering field and designed the CUDA kernel function to implement a computation parallelization for SAR echo generation. All the echo matrix cells are traversed in parallel to obtain the total backscattering field of the target, reproducing the time-varying characteristic of the backscatter coefficient for each lattice target within the synthetic aperture time. The echo signal is processed using the RD imaging algorithm to obtain the simulated SAR image. Finally, we select some targets including aircraft carrier and airplane models for simulation tests. The computation efficiency is improved over 170-fold by comparing the computations of the proposed method and CPU single-thread. We also performed some qualitative and quantitative evaluations on the fidelity of the simulated SAR images. The experimental results verify the effectiveness of the proposed method.

An Hybrid Integration Method-Based Track-before-Detect for High-Speed and High-Maneuvering Targets in Ubiquitous Radar

Due to the limited transmission gain of ubiquitous radar systems, it has become necessary to use a long-time coherent integration method for range-Doppler (RD) analysis. However, when the target exhibits high-speed and high-maneuver capabilities, it introduces challenges, such as range migration (RM), Doppler frequency migration (DFM), and velocity ambiguity (VA) in the RD domain, thus posing significant difficulties in target detection and tracking. Moreover, the presence of VA further complicates the problem. To address these complexities while maintaining integration efficiency, this study proposes a hybrid integration approach. First, methods called Keystone-transform (KT) and matched filtering processing (MFP) are proposed for compensating for range migration (RM) and velocity ambiguity (VA) in Radar Detection (RD) images. The KT approach is employed to compensate for RM, followed by the generation of matched filters with varying ambiguity numbers. Subsequently, MFP enables the production of multiple RD images covering different but contiguous Doppler frequency ranges. These RD images can be compiled into an extended RD (ERD) image that exhibits an expanded Doppler frequency range. Second, an improved particle-filter (IPF) algorithm is raised to perform incoherent integration among ERD images and to achieve track-before-detect (TBD) for a target. In the IPF, the target state vector is augmented with ambiguous numbers, which are estimated via maximum posterior probability estimation. Then, to compensate for the DFM, a line spread model (LSM) is proposed instead of the point spread model (PSM) used in traditional PF. To evaluate the efficacy of the proposed method, a radar simulator is devised, encompassing comprehensive radar signal processing. The findings demonstrate that the proposed approach achieves a harmonious equilibrium between integration efficiency and computational complexity when it comes to detecting and tracking high-speed and high-maneuvering targets with intricate maneuvers. Furthermore, the algorithm’s effectiveness is authenticated by exploiting ubiquitous radar data.

Method of 2-D Range-Doppler Imaging for Plasma Wake Based on Range Walk Correction

When a hypersonic vehicle flies in the atmosphere, plasma sheath and trails are configured around the vehicle due to aerodynamic heating and ablation, which distort the radar echo signal and then influence the target range information and Doppler frequency extracted from the echoes. In order to examine the impact of plasma laminar wake on radar detection, this article is primarily aimed to establish a laminar wake echo model based on a linear frequency-modulated continuous wave (LFMCW) radar, which takes into account the reaction control system of laminar wake by employing the Born approximation solution and the characteristics of the inhomogeneous and nonunique velocity of the wake medium. An echo signal processing method is also developed, in which the stretch processing technique is combined with the keystone transform of the target range walk correction. Such a treatment not only reduces the sampling rate and bandwidth, but also eliminates the cross-range cells phenomenon caused by hypersonic; finally, the range-Doppler (RD) image of the wake is achieved by 2-D fast Fourier transform (2-D FFT) processing, and radar range and velocity measurement results in the presence of plasma are obtained.

Robust iterative adaptive filtering against intrapulse Doppler shifts

Recently, a two-dimensional joint iterative adaptive filtering (2-D JIAF) has been proposed to address the masking effect of large targets on adjacent small targets in multi-target scenarios. However, its performance is impaired for the cases with fast moving targets due to intrapulse Doppler mismatch. In this paper, we consider the intrapulse Doppler shifts in filtering design and propose a robust iterative adaptive filtering algorithm based on matched filter outputs, termed robust iterative adaptive filtering against intrapulse Doppler shifts (RIAF-IDS), to suppress the range sidelobes induced by intrapulse Doppler mismatch and improve the filtering performance. The proposed algorithm can jointly suppress range-Doppler sidelobes and obtain accurate estimation of range-Doppler image even in cases with fast moving targets. The derivation of RIAF-IDS is provided in detail, and its filtering performance is validated through several simulations, which demonstrate that RIAF-IDS has superior Doppler tolerance over 2-D JIAF at the cost of more computation.

Low-Complexity Iterative Adaptive Approach Based on Range–Doppler Matched Filter Outputs

This article presents a computationally efficient iterative adaptive approach based on range–Doppler matched filter outputs for sidelobe suppression and range–Doppler imaging. A sidelobe suppression scheme, named as dimension reduction based fast iterative adaptive approach (DR-FIAA), is designed by adopting a small processing window on range–Doppler matched filter outputs to eliminate the masking of weak targets by strong targets nearby with low computational complexity. Two specific methods are proposed under this scheme, namely synchronous FIAA (SY-FIAA) and sequential FIAA (SE-FIAA). Compared to SY-FIAA, SE-FIAA has lower computational complexity at the cost of some performance loss. Based on the structure relationships among covariance matrices, further reduced computational complexity can be achieved by SY-FIAA and SE-FIAA. Numerical examples for different scenarios are included to demonstrate the effectiveness of the proposed designs.

An Improved Range-Doppler Imaging Algorithm Based on High-Order Range Model for Near-Field Panoramic Millimeter-Wave ArcSAR

An Improved Range-Doppler Imaging Algorithm Based on High-Order Range Model for Near-Field Panoramic Millimeter-Wave ArcSAR

Complex Mutual Information-Based Least-Dependent Component Analysis for ISAR Imaging of Multiple Targets in a Formation Flight

This article proposes a signal decomposition-based approach for inverse synthetic aperture radar (ISAR) imaging of multiple targets in a formation. The procedure of the proposed framework is divided into three steps. The first step is to preprocess the radar echoes received by the spatial multichannel radar via pulse compression. The second step is signal decomposition using the complex-valued mutual information-based least-dependent component analysis proposed in this article. In this step, the range profiles (RPs) of multiple targets are separated into individual RPs to generate an ISAR image for each target. The third step is range-Doppler imaging using the separated RPs. As compared to the conventional methods, the proposed method decomposes the superimposed radar echoes at the raw signal level. Therefore, even if multiple targets overlap in the ISAR image, or RP histories are unaligned owing to the change in the relative positions between multiple targets, a well-focused ISAR image can still be generated. Simulation results using three targets composed of point scatterer centers verify that the proposed method can effectively segment three targets closely flying in a formation.

Development of a Technique for Classifying Photovoltaic Panels Using Sentinel-1 and Machine Learning

With the increasing interest in effective renewable alternative energy sources resulting from the Paris Agreement on Climate Change in 2015, photovoltaic (PV) power generation is attracting attention as a practical measure. In this study, we develop procedures for efficiently monitoring PV panels in a large area and increasing their classification accuracy to enable efficient management of PV panels, an important component of renewable energy generation. To accomplish this, first, the persistent scatterer characteristics (e.g., polarization, imaging module, and topography) of PV panels in SAR images were utilized. Then, we developed a technique for classifying panels over a certain size using the polarization and pulse-scattering characteristics of Sentinel-1. Next, by stacking Sentinel-1 ground range Doppler (GRD) images and comparing them with the surroundings of the same area, the morphological features of PV panels were derived and built as learning data for machine learning. Then, a more precise classification of PV panels was performed by applying these learning data in AI algorithms. When SAR-based AI training data for the same PV panels were used in the YOLOv3 and YOLOv5 algorithms, both algorithms showed high accuracy of over 90%, but there were differences in precision and recall. These findings will enable more efficient monitoring of PV panels, the use of which is expected to increase in the future. In addition, they can serve as a proactive response tool to address environmental problems such as PV panel waste and panels washed away during natural disasters.

Genetic algorithm-based mathematical morphology for clutter removal in airborne radars

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.

SAR Image Simulation Based on Effective View and Ray Tracing

We present a novel echo-based synthetic aperture radar (SAR) image simulation method that comprehensively utilizes both SAR effective view and ray tracing algorithms. To improve the fidelity of simulated SAR images, we first design an SAR effective view algorithm to process the selected facet target model, with the purpose of discretizing the facets in the SAR effective view into lattice targets and ensuring that the interval of lattice targets is set strictly following the Nyquist sampling law. Then, we combine the ray tracing algorithm and SAR echo time-domain simulation and perform SAR echo time-domain simulation of lattice targets based on ray tracing. Various kinds of backscatter coefficient for each point target can be recorded, corresponding to the number of transmitted pulses within the synthetic aperture time. The echo matrixes of lattice targets are superimposed to generate the raw echo signal. Finally, the raw echo signal is processed by using the range-Doppler (RD) imaging algorithm to obtain the simulated SAR image. We conducted SAR image simulation tests on several facet target models, including car body, assault boat, and airplane, with different material properties. The simulated SAR images obtained by the proposed method were qualitatively and quantitatively evaluated. The experimental results verify the effectiveness of the proposed method.

MmGesture: Semi-supervised gesture recognition system using mmWave radar

Gesture recognition has found versatile applications in natural human–computer interaction (HCI). Compared with traditional camera-based or wearable sensors-based solutions, gesture recognition using the millimeter wave (mmWave) radar has attracted growing attention for its characteristics of contact-free, privacy-preserving and less environment-dependence. Recently, most of studies adopted one of the Range Doppler Image (RDI), Range Angle Image (RAI), Doppler Angle Image (DAI) or Micro-Doppler Spectrogram extracted from the raw radar signal as the input of a deep neural network to realize gesture recognition. However, the effectiveness of these four inputs in gesture recognition has attracted little attention so far. Moreover, the lack of large amounts of labeled data restricts the performance of traditional supervised learning network. In this paper, we first conducted extensive experiments to compare the effectiveness of these four inputs in the gesture recognition, respectively. Then we proposed a semi-supervised leaning framework by utilizing few labeled data in the source domain and large amounts of unlabeled data in the target domain. Specially, we combine the ∏-model and some specific data augmentation tricks on the mmWave signal to realize the domain-independent gesture recognition. Extensive experiments on a public mmWave gesture dataset demonstrate the superior effectiveness of the proposed system.