Radar Echo Signal Research Articles

기상 클러터 효과를 고려한 표면 클러터 반사신호 모노펄스 레이다 공대지 신호 생성

An airborne radar echo signal includes signals returned from clutter and a target; thus, simulating the signal from the clutter may be crucial for predicting and analyzing accurate radar performance. Because it can be extremely difficult and expensive to obtain real measured signal data for various scenarios, an accurate signal generation method is required. Existing weather clutter and ground clutter simulators for an X-band airborne radar are combined to simulate the echo signal when two clutters exist simultaneously. The weather clutter effect becomes more significant with increasing frequencies, such as in the X-band. The proposed simulator can generate time-domain signals by considering the random characteristics of the clutter signal and the Doppler effect caused by the movement of aircraft and clutter. In addition, a simple scheme is proposed for generating signals from artificial structures. Finally, the finite bandwidth of the transmitted chirp signal is calculated to improve the accuracy of the generated signal.

Time Domain Signal Simulation of Weather Clutter for an Airborne Radar Based on Measured Data

An airborne radar simulator for an X-band radar echo signal was implemented and numerically verified. The simulator can consider weather conditions over a large area and can use measured S-band data as input. A scheme is presented for the conversion of the S-band data to the X-band one. The amplitude and time delay due to weather particles can be theoretically calculated on the basis of reflectivity. The dynamics of the platform and clutter particles were modeled using the Doppler frequency shift in the echo signal. As the simulated weather area was very large, an efficient approximation method was designed for use in calculating the attenuation and time delay, which was also numerically verified. Finally, a scheme for extracting the reflectivity from the echo signal was formulated and was verified by comparing it with the input data.

Radar Target Detection Based on Waveform Design under Atmospheric Attenuation

Under the condition of atmospheric attenuation, the mismatch of radar echo signals leads to the decrease of signal-to-noise ratio (SNR) and detection probability. A joint optimization algorithm for transmitting signal and receiving filter of MIMO radar with atmospheric system response is presented in this paper. Considering the energy constraint, similarity constraint and spectral coexistence constraint of the transmitting signal, and introducing the atmospheric channel response matrix, the echo model under the atmospheric response condition is constructed. By optimizing the transmitting signal and the receiving filter, the echo signal SNR and radar detection probability can be improved. On this basis, the ARMA stochastic model is used to simulate the atmospheric system response. Solve non-convex problems by matrix optimization, positive semi-definite relaxation, Charnes-Cooper transformation, and loop optimization methods. Finally, the rank-one matrix decomposition method is used to extract the optimal signal from the signal autocorrelation matrix. And we analyze the convergence of the algorithm, the degree of SNR improvement and the detection probability. Through numerical experiments, it is proved that after the atmospheric channel response is introduced and matched correctly, the SNR of the echo signal can be improved and the radar detection probability can be greatly increased.

ML-HGR-Net: A Meta-Learning Network for FMCW Radar Based Hand Gesture Recognition

In recent years, radar-based hand gesture recognition (HGR) has attracted much attention in the field of human-computer interaction (HCI) due to its benefits of high recognition accuracy and independence from lighting conditions. Conventional deep learning (DL) based models for HGR rely on a large amount of labelled data for training to achieve a satisfactory recognition accuracy. However, it is usually time-consuming and labor-intensive for collecting large amounts of radar data and also difficult to cover all possible hand gesture classes. In this paper, we introduce meta-learning to address the few-shot learning problem for frequency modulated continuous wave (FMCW) radar based HGR. We propose a meta-learning network to learn a model that can quickly adapt to unseen hand gesture tasks with few training observations. In particular, we propose a 3D convolutional neural network (CNN) based dual-channel fusion network for feature extraction by exploiting the correlations of multiple features in radar echo signals to improve the recognition accuracy. In addition, we also develop a learnable relation module with neural networks as a non-linear classifier to measure the similarity between the samples of different hand gestures, which can be more applicable for the HGR task. Finally, we evaluate the performance of the proposed model by conducting experiments on an FMCW radar hardware system. Experimental results show that the proposed meta-learning model substantially enhances the recognition accuracy compared with the state-of-the-art methods including DL and meta-learning based models for FMCW radar-based HGR.

Fully integrated hybrid microwave photonic receiver

Microwave photonic receivers are a promising candidate in breaking the bandwidth limitation of traditional radio-frequency (RF) receivers. To further balance the performance superiority with the requirements regarding size, weight, and power consumption (SWaP), the implementation of integrated microwave photonic microsystems has been considered an upgrade path. However, up to now, to the best of our knowledge, chip-scale fully integrated microwave photonic receivers have not been reported due to the limitation of material platforms. In this paper, we report a fully integrated hybrid microwave photonic receiver (FIH-MWPR) obtained by comprising the indium phosphide (InP) laser chip and the monolithic silicon-on-insulator (SOI) photonic circuit into the same substrate based on the low-coupling-loss micro-optics method. Benefiting from the integration of all optoelectronic components, the packaged FIH-MWPR exhibits a compact volume of 6 cm 3 and low power consumption of 1.2 W. The FIH-MWPR supports a wide operation bandwidth from 2 to 18 GHz. Furthermore, its RF-link performance to down-convert the RF signals to the intermediate frequency is experimentally characterized by measuring the link gain, the noise figure, and the spurious-free dynamic range metrics across the whole operation frequency band. Moreover, we have utilized it as a de-chirp receiver to process the broadband linear frequency-modulated (LFM) radar echo signals at different frequency bands (S-, C-, X-, and Ku-bands) and successfully demonstrated its high-resolution-ranging capability. To the best of our knowledge, this is the first realization of a chip-scale broadband fully integrated microwave photonic receiver, which is expected to be an important step in demonstrating the feasibility of all-integrated microwave photonic microsystems oriented to miniaturized application scenarios.

Parameter Estimation for Precession Cone-Shaped Targets Based on Range–Frequency–Time Radar Data Cube

A radar echo signal received from a cone-shaped target with precession contains micro-Doppler (m-D) information from different effective scattering centers. By taking full advantage of the m-D information, this paper proposes a parameter estimation algorithm for precession cone-shaped targets based on the range–frequency–time radar data cube (RDC). We build scattering center models of precession cone-shaped targets with the occlusion effect. The Binary Mask method is first utilized to obtain a high-resolution range-Doppler (RD) sequence. On this basis, the range–frequency–time RDC can be extracted from the RD sequence. In order to approach the actual case, we discuss the parameter estimation algorithm under different radar lines-of-sight (LOS). The most attractive attribute of this algorithm is that it can conduct in-depth research on m-D parameter estimation from a three-dimensional (3D) domain. Finally, the experimental results illustrate the effectiveness of the proposed method.

Signal Simulation of Dual-Polarization Weather Radar and Its Application in Range Ambiguity Mitigation

In this paper, a dual-polarization weather radar echo signal simulation method is proposed for the evaluation of the performance enhancement of dual-polarization weather radar systems, the optimization of signal processing algorithms and the improvement of scanning strategies. The actual weather radar base data are used in the simulation as the reference weather scene, which avoids using a complex algorithm for weather modeling. Moreover, based on radar weather equations, the radar system parameters are added into the radar echo signal modeling to establish the relationship between the simulated echo signal and radar system. As a result, the final simulated echo signal not only shows both the time and frequency domain characteristics of the weather target, but also includes the effects of the important performance of the dual-polarization weather radar system. In addition, to evaluate the performance of range ambiguity mitigation using phase coding and batch working modes, two different simulation methods for the radar signal are established on the method above; one is for batch working mode with long-PRT (pulse repetition time) and short-PRT transmission and receiving, and the other is for phase-coded mode with phase-coded transmission and phase-uncoded receiving. Under the same weather scene, the observation of these two different methods of range ambiguity mitigation are simulated and compared. Results show that the performance of the phase coding mode for mitigating range ambiguity is better than that of the batch mode. Obviously, the simulation method can be used to directly show the observation of different algorithms for mitigation range ambiguity under the same weather process, and quickly compare and evaluate the algorithm’s performance, which is not possible for real radars.

Physical model-driven deep networks for through-the-wall radar imaging

AbstractIn order to merge the advantages of the traditional compressed sensing (CS) methodology and the data-driven deep network scheme, this paper proposes a physical model-driven deep network, termed CS-Net, for solving target image reconstruction problems in through-the-wall radar imaging. The proposed method consists of two consequent steps. First, a learned convolutional neural network prior is introduced to replace the regularization term in the traditional iterative CS-based method to capture the redundancy of the radar echo signal. Moreover, the physical model of the radar signal is used in the data consistency layer to encourage consistency with the measurements. Second, the iterative CS optimization is unrolled to yield a deep learning network, where the weight, regularization parameter, and the other parameters are learnable. A quantity of training data enables the network to extract high-dimensional characteristics of the radar echo signal to reconstruct the spatial target image. Simulation results demonstrated that the proposed method can achieve accurate target image reconstruction and was superior to the traditional CS method, in terms of mean squared error and the target texture details.

Sensing-Assisted Secure Uplink Communications With Full-Duplex Base Station

This letter proposes a sensing-assisted uplink communications framework between a single-antenna user and a full-duplex (FD) base station (BS) against an aerial eavesdropper (AE). To protect the information from being overheard, the BS transmits radar signals to localize and jam AE while receiving uplink signals. The radar signal transmission is divided into detection phase and tracking phase. In detection phase, the BS synthesizes a wide beam to localize the AE under the secrecy rate constraint; while in tracking phase, the BS maximizes the signal-to-interference-plus-noise ratio (SINR) of its received signals under the AE’s SINR constraint while guaranteeing a predefined radar echo signal signal-to-noise ratio (SNR) level. To deal with the self interference, we jointly optimize the radar waveform and receive beamforming vector. An alternating optimization algorithm and a successive convex approximation (SCA) based algorithm are proposed to solve the two formulated problems, respectively. Simulation results verify the effectiveness of the proposed algorithms. They also show that the secrecy rate can be significantly improved with the assistance of BS sensing.

ISAR Imaging of a Maneuvering Target Based on Parameter Estimation of Multicomponent Cubic Phase Signals

In inverse synthetic aperture radar (ISAR) imaging for a uniformly moving rigid-body target, a finely focused ISAR image can be obtained by using the conventional range-Doppler algorithm. However, the ISAR image quality may significantly deteriorate when the time-vary Doppler phases in virtue of target maneuvering motions are present, such as an airplane with nonuniformly rotation and a ship with fluctuation. This has become a challenging task, especially under nonhigh signal-to-noise ratio (SNR) environment. In this article, a novel ISAR imaging algorithm for a maneuvering target with moderate reflection intensity is proposed. After motion compensation, the radar echo signal in a range cell is modeled as a multicomponent cubic phase signal (CPS), in which the chirp rate and the quadratic chirp rate are two important physical quantities that may determine the target ISAR focusing quality. Based on a symmetrical instantaneous autocorrelation function, the received CPSs are transformed into the time and lag-time plane, and then a 2-D coherent integration can be realized after the generalized time-scaled transform and 1-D maximization. This forms a high-quality ISAR image. The effectiveness and superiority of the proposed algorithm are validated by the ISAR imaging results of simulated and real measured data.

Radar HRRP Target Recognition Model Based on a Stacked CNN–Bi-RNN With Attention Mechanism

The range resolution of high-resolution wideband radar is much smaller than the target size. Its echo signals tend to be diverse and sensitive to small changes of targets. Therefore, it is difficult to capture and distinguish the features in radar signals. In this article, we propose a radar target recognition pipeline based on a deep nested neural network. The framework consists of three parts: The translation sensitivity of the training data is first addressed in the preprocessing section. The second step is to obtain an embedded representation of the radar echo signals by the combination of the adjustment layer, convolutional neural network (CNN), and the squeeze and excitation (SE) block. Finally, the target is recognized through inputting embedded representation as a time sequence into the stacked bidirectional recurrent neural network (bi-RNN) based on an attention mechanism. Compared with the traditional methods, the proposed deep nested neural network extracts and takes advantage of the features of radar echo signals more effectively, including the envelope features and local physical structural features. The experimental results based on the test data indicate that the proposed method has a great advantage over other methods in the case of large data sets as well as small training data sets and is robust to the small translation of test samples and noises, exhibiting high engineering practical value.

An Improved Radar Echo Signal Processing Algorithm for Industrial Liquid Level Measurement

In this paper, based on the study of Linear Frequency Modulated Continuous Wave (LFMCW) radar, a ranging algorithm for the high-precision liquid level measurement in industry is proposed. A new algorithm is developed based on the MacLeod algorithm. The frequency offset δ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> is estimated, and then (δ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -q, δ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> +q) is used as the iterative estimation initial iteration interval to determine the frequency offset δ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> more precisely. The improved algorithm is the Golden-MacLeod algorithm, referred to as the GM algorithm. Simulation results show that the GM can effectively reduce the estimation error and maintain the high frequency estimation accuracy at signal-to-noise ratio (SNR) up to -10 dB, which is better than the MacLeod algorithm. In addition, the estimation accuracy, stability and noise immunity are better than other algorithms in the variation interval of SNR greater than -5 dB. Liquid level measurement experiment is tested by TI’s IWR 6843ISK and DCA1000EVM. The measurement results also show that the measurement accuracy of the GM can meet the requirements of industrial applications. This also proves the reliability of the GM in practical applications.

A Numerical Simulation Method of Radar Echo From a High-Speed Target

The radar echo signal of a high-speed target is simulated in this letter. Using the Lorentz transformation, the linear frequency modulation pulse radar signal is transformed from the laboratory coordinate system into a moving system. The near-zone scattered fields and the far-zone echo signals are solved by the finite-difference time-domain (FDTD) algorithm in the moving system that is kept the same velocity as the target. Subsequently, transform the echo signal back to the laboratory system. Meanwhile, this letter combines the graphic processing unit parallel technique with the Lorentz–FDTD method to realize the simulation of a high-speed target excited by a large time-width signal and extracts the one-dimensional(1-D) range profile of the target. The validity of the proposed method is verified by the analytical results of a high-speed target excited by arbitrary directions incident waves. The numerical examples show that the motion's effects on the range information of the radar signal are nonnegligible for high-speed objects.

자탄 분산에 대한 도플러 레이다 신호 시뮬레이션

To accurately simulate signals scattered by multiple targets and received by Doppler radar, target motion under aerodynamic forces has to be modeled. We present a new technique for computing aerodynamic forces acting on flying mines of remote anti-armor mine systems (RAAMS). This weapon system is particularly attractive for detailed analysis of multiple clustered sub-munition systems, owing to their simple shape and relatively small number. The generated positions and velocities of multiple mines are then used to generate the radar echo signal received by the Doppler radar.

A Transfer Learning Approach for Compressed Sensing in 6G-IoT

The data in the Internet of Things (IoT) and the sixth-generation (6G) wireless networks increase dramatically with higher dimensions compared to the traditional wireless networks. Compressed sensing (CS) has been adopted to effectively reduce the amount of transmitting signal with sparsity and recover accurately at the receiver. It has been proved that better recovery performance can be achieved via deep learning-based CS approaches. However, these methods require a mass of relevant data to train neural networks (NNs), not adapted for the case of small sample data. In this article, a convolution-based transfer learning CS (CTCS) model is proposed to reconstruct the compressed signal based on transfer learning. Ultrawide band (UWB) radar echo signal and Mnist hand-written data set are selected to evaluate the performance of CTCS. It is verified that the proposed model outperforms other traditional reconstruction algorithms in 6G-IoT under different noise levels, measurement numbers, and signal sparsities.

On the Effect of Training Convolution Neural Network for Millimeter-Wave Radar-Based Hand Gesture Recognition.

The purpose of this paper was to investigate the effect of a training state-of-the-art convolution neural network (CNN) for millimeter-wave radar-based hand gesture recognition (MR-HGR). Focusing on the small training dataset problem in MR-HGR, this paper first proposed to transfer the knowledge with the CNN models in computer vision to MR-HGR by fine-tuning the models with radar data samples. Meanwhile, for the different data modality in MR-HGR, a parameterized representation of temporal space-velocity (TSV) spectrogram was proposed as an integrated data modality of the time-evolving hand gesture features in the radar echo signals. The TSV spectrograms representing six common gestures in human–computer interaction (HCI) from nine volunteers were used as the data samples in the experiment. The evaluated models included ResNet with 50, 101, and 152 layers, DenseNet with 121, 161 and 169 layers, as well as light-weight MobileNet V2 and ShuffleNet V2, mostly proposed by many latest publications. In the experiment, not only self-testing (ST), but also more persuasive cross-testing (CT), were implemented to evaluate whether the fine-tuned models generalize to the radar data samples. The CT results show that the best fine-tuned models can reach to an average accuracy higher than 93% with a comparable ST average accuracy almost 100%. Moreover, in order to alleviate the problem caused by private gesture habits, an auxiliary test was performed by augmenting four shots of the gestures with the heaviest misclassifications into the training set. This enriching test is similar with the scenario that a tablet reacts to a new user. The results of two different volunteer in the enriching test shows that the average accuracy of the enriched gesture can be improved from 55.59% and 65.58% to 90.66% and 95.95% respectively. Compared with some baseline work in MR-HGR, the investigation by this paper can be beneficial in promoting MR-HGR in future industry applications and consumer electronic design.

FFBV Algorithm Design for Soft Synchronization FMCW Radar

Whether in military or civilian use, frequency-modulated continuous-wave (FMCW) radars play a very important role, but common FMCW radars need to collect both radar echo signals and hardware synchronization signals during echo signal processing. Therefore, this article proposes a new synchronization algorithm, i.e., the four-threshold frequency band variance (FFBV) synchronization algorithm. Then, we combine theoretical derivation and experimental verification to analyze the performance of the FFBV synchronization algorithm. With assistance of the FFBV synchronization algorithm, the soft synchronization FMCW radar can achieve the same functions without hardware synchronization signals. First, we analyze the principle behind the FMCW radar based on intermittent ramp signals. Second, we analyze the design of the FFBV synchronization algorithm. In addition, we verify the performance of the algorithm when the received signal is corrupted by the additive white Gaussian noise (AWGN). Then, we analyze the performance from two aspects, including the performance of the algorithm under different signal-to-noise ratio (SNR) conditions, as well as the performance comparison with other common synchronization algorithms. Finally, experimental results show that the FFBV synchronization algorithm has achieved good performance in solving the signal synchronization issue of the FMCW radar under the condition of low SNR. Especially, the SNR required by the FFBV synchronization algorithm is 2 and 6.5 dB lower than that required by the three-threshold synchronization algorithm and variance synchronization algorithm, respectively, under the same condition that the accuracy is 90%.

Compressed Sensing Reconstruction of Radar Echo Signal Based on Fractional Fourier Transform and Improved Fast Iterative Shrinkage‐Thresholding Algorithm

The compressed sensing theory, which has received great attention in the field of radar technology, can effectively reduce the data rate of high‐resolution radar imaging systems and solve the problem of collecting, storing, and transmitting large amounts of data in radar systems. Through the study of radar signal processing theory, it can be found that the echo of radar LFM transmit signal has sparse characteristics in the distance upward; based on this, we can consider using the theory of compressed sensing in the processing of radar echo to optimize the processing. In this paper, a fast iterative shrinkage‐thresholding reconstruction algorithm based on protection coefficients is proposed. Under the new scheme, firstly, the LFM echo signal’s good sparse representation is obtained by using the time‐frequency sparse characteristics of the LFM echo signal under the fractional Fourier transform; all reconstruction coefficients are analyzed in the iterative process. Then, the coefficients related to the feature will be protected from threshold shrinkage to reduce information loss. Finally, the effectiveness of the proposed method is verified through simulation experiments and application example analysis. The experimental results show that the reconstruction error of this method is lower and the reconstruction effect is better compared with the existing reconstruction algorithms.

A SAR Target Image Simulation Method With DNN Embedded to Calculate Electromagnetic Reflection

Electromagnetic (EM) scattering calculation is a very important part of most synthetic aperture radar (SAR) target image simulation methods. It affects the intensity of the radar echo signal to a great extent, thus affecting the quality of the final simulation image. EM reflection models are usually approximate formulas derived under certain assumptions. The errors between these models and the actual situation can cause significant differences between simulated images and real images. To solve this problem, we propose a novel modified SAR target image simulation framework, in which the deep neural network (DNN) is embedded to calculate the EM reflection, so that the DNN can directly learn and fit the EM reflection models from real SAR images. First, the intensity calculation of radar signal in a single reflection is separated from the cumulative calculation of multiple radar reflection signals intensity in each pixel. Thus, the approximate calculation formulas of EM reflection can be replaced with the DNN models. Next, the DNN model is trained with the backpropagation algorithm to learn the actual EM reflection model from real SAR images. Finally, the fitted EM reflection models and an image post-processing model are applied to simulate images of the target under different imaging angles. In the simulation framework, the functions of the neural network models are limited to calculating the reflection coefficient and adding sidelobe and speckle noise. The imaging model is still the original simulation method based on ray tracing, which ensures the correctness and generalization of the simulation method. Experiments show that the proposed simulation method can significantly improve the quality of the simulation image. When the image is normalized to [0, 1], the minimum mean square error between the simulated SAR images and the real images of the Sandia laboratory implementation of cylinders target can reach 0.003. The visualization results of the DNN models show that the fitted reflection coefficient calculation curve and the convolution kernel used for image post-processing are consistent with the laws in the theoretical model. In addition, when the proposed method is used to simulate complex targets, the similarity of simulation images can also be significantly improved.

Non-Linear Frequency Modulated Radar Echo Signal Cancellation using Interrupted Sampling Repeater Jamming

Non-Linear Frequency Modulated Radar Echo Signal Cancellation using Interrupted Sampling Repeater Jamming