scholarly journals Neural Network-Guided Sparse Recovery for Interrupted-Sampling Repeater Jamming Suppression

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zijian Wang ◽  
Wenbo Yu ◽  
Zhongjun Yu ◽  
Yunhua Luo ◽  
Jiamu Li
Keyword(s):  
Neural Network ◽  
Pulse Compression ◽  
Radar Signal ◽  
Echo Signal ◽  
Target Groups ◽  
The Real ◽  
Processing Gain ◽  
False Target ◽  
Target Locating ◽  
Real Target

Interrupted-sampling repeater jamming (ISRJ) is a new type of DRFM-based jamming designed for linear frequency modulation (LFM) signals. By intercepting the radar signal slice and retransmitting it many times, ISRJ can obtain radar coherent processing gain so that multiple false target groups can be formed after pulse compression (PC). According to the distribution characteristic of the echo signal and the coherence of ISRJ to radar signal, a new method for ISRJ suppression is proposed in this study. In this method, the position of the real target is determined using a gated recurrent unit neural network (GRU-Net), and the real target can be, therefore, reconstructed by adaptive filtering in the sparse representation of the echo signal based on the target locating result. The reconstruction result contains only the real target, and the false target groups formed by ISRJ are suppressed completely. The target locating accuracy of the proposed GRU-Net can reach 92.75%. Simulations have proved the effectiveness of the proposed method.

Preceded False Target Groups Jamming Against LFM Pulse Compression Radars

2011 ◽  
Vol 30 (6) ◽  
pp. 1350-1353 ◽  
Author(s):  
Jian-cheng Liu ◽  
Xue-song Wang ◽  
Zhong Liu ◽  
Jian-hua Yang ◽  
Guo-yu Wang
Keyword(s):  
Pulse Compression ◽  
Target Groups ◽  
False Target

scholarly journals Parameter Estimation for Interrupted Sampling Repeater Jamming Based on ADMM

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8277
Author(s):  
Chaoyu Wang ◽  
Wanwan Hu ◽  
Zhe Geng ◽  
Jindong Zhang ◽  
Daiyin Zhu
Keyword(s):  
Optimization Model ◽  
Pulse Compression ◽  
Original Problem ◽  
Analytical Form ◽  
Radar Signal ◽  
Integer Optimization ◽  
Digital Radio ◽  
Time Frequency ◽  
Alternating Direction ◽  
False Target

By repeatedly sampling, storing, and retransmitting parts of the radar signal, interrupted sampling repeater jamming (ISRJ) based on digital radio frequency memory (DRFM) can produce a train of secondary false targets symmetrical to the main false target, threatening to mislead or deceive the victim radar system. This paper proposes a computationally-effective method to estimating the parameters for ISRJ by resorting to the framework of alternating direction method of multipliers (ADMM). Firstly, the analytical form of pulse compression is derived. Then, for the purpose of estimating the parameters of ISRJ, the original problem is transformed into a nonlinear integer optimization model with respect to a window vector. On this basis, the ADMM is introduced to decompose the nonlinear integer optimization model into a series of sub-problems to estimate the width and number of ISRJ’s sample slices. Finally, the numerical simulation results show that, compared with the traditional time-frequency (TF) method, the proposed method exhibits much better performance in accuracy and stability.

scholarly journals Countering a Self-protection Frequency-shifting Jamming against LFM Pulse Compression Radars

2017 ◽  
Vol 63 (2) ◽  
pp. 145-150 ◽  
Author(s):  
S. Baher Safa Hanbali ◽  
Radwan Kastantin
Keyword(s):  
Pulse Compression ◽  
Radar Signal ◽  
True Target ◽  
Processing Gain ◽  
Before And After ◽  
A New Technique ◽  
Small Change ◽  
Pulse Compression Radar ◽  
Self Protection ◽  
Frequency Shifting

Abstract the well-known range-Doppler coupling property of the LFM (Linear Frequency Modulation) pulse compression radar makes it more vulnerable to repeater jammer that shifts radar signal in the frequency domain before retransmitting it back to the radar. The repeater jammer, in this case, benefits from the pulse compression processing gain of the radar receiver, and generates many false targets that appear before and after the true target. Therefore, the radar cannot distinguish between the true target and the false ones. In this paper, we present a new technique to counter frequency shifting repeater jammers. The proposed technique is based on introducing a small change in the sweep bandwidth of LFM waveform. The effectiveness of the proposed technique is justified by mathematical analysis and demonstrated by simulation.

scholarly journals Improved ISRJ-Based Radar Target Echo Cancellation Using Frequency Shifting Modulation

Electronics ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 46 ◽  
Author(s):  
Qihua Wu ◽  
Feng Zhao ◽  
Junjie Wang ◽  
Xiaobin Liu ◽  
Shunping Xiao
Keyword(s):  
Frequency Modulation ◽  
Sampling Frequency ◽  
Measured Data ◽  
Echo Cancellation ◽  
Radar Signal ◽  
Radar Target ◽  
False Target ◽  
Ingenious Method ◽  
Real Target ◽  
Frequency Shifting

Target echo cancellation is an ingenious method that protects the target of interest (TOI) from being detected by radar. Interrupted-sampling repeater jamming (ISRJ) is a novel deception jamming method for linear frequency modulation (LFM) radar countermeasures, which has been applied in target echo cancellation recently. Compared with the conventional cancellation method, not only can the target echo be successfully cancelled at radar receiver, but a train of false targets is also produced and forms deception jamming by applying the ISRJ technique. In this paper, an improved radar target echo cancellation method based on ISRJ is proposed that utilizes an extra frequency shifting modulation on the intercepted LFM radar signal. The jammer power is more efficiently utilized by the proposed method. Moreover, more flexible multi-false-target deception jamming can be obtained by adjusting the interrupted sampling frequency. The real target remains effectively protected by the false preceding target in the presence of amplitude mismatch of cancellation signal and target echo. Numerical simulations and measured data experiments are conducted to demonstrate the effectiveness of the proposed method.

A New Radar Signal Processing Architecture Based on Multi-Core Processor

2014 ◽  
Vol 556-562 ◽  
pp. 1618-1621
Author(s):  
Jia Liang Fan ◽  
Qiang Yang
Keyword(s):  
Signal Processing ◽  
Pulse Compression ◽  
Adaptive Beamforming ◽  
System Structure ◽  
Radar Signal ◽  
Radar Signal Processing ◽  
Test Results ◽  
Radar Systems ◽  
Multi Core Processor ◽  
Processing Architecture

Most radar systems based on the structure that contains many DSP chips. The system structure is always complex, and it is difficult to update. Nowadays, multi-core processor develops very fast. Compared with DSP chips, multi-core processor has better performance in signal processing field. In this paper, we present a signal processing architecture which based on multi-core processor. Pulse compression algorithms and PCI-E bus are discussed as two important technologies. Adaptive beamforming test results show that multi-core processor is able to achieve radar signal processing.

scholarly journals Improving the Learning of Self-driving Vehicles Based on Real Driving Behavior using Deep Neural Network Techniques

2020 ◽  
Author(s):  
Nayere Zaghari ◽  
Mahmood Fathy ◽  
Seyed Mahdi Jameii ◽  
Mohammad Sabokrou ◽  
Mohammad Shahverdy
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Autonomous Vehicle ◽  
Driving Behavior ◽  
Obstacle Detection ◽  
Convolutional Network ◽  
The Real ◽  
Cloning Method ◽  
Vehicle Acceleration ◽  
Performance Results

Considering the significant advancements in autonomous vehicle technology, research in this field is of interest to researchers. To drive vehicles autonomously, controlling steer angle, gas hatch, and brakes need to be learned. The behavioral cloning method is used to imitate humans’ driving behavior. We created a dataset of driving in different routes and conditions and using the designed model, the output used for controlling the vehicle is obtained. In this paper, the Learning of Self-driving Vehicles Based on Real Driving Behavior Using Deep Neural Network Techniques (LSV-DNN) is proposed. We designed a convolutional network which uses the real driving data obtained through the vehicle’s camera and computer. The response of the driver is during driving is recorded in different situations and by converting the real driver’s driving video to images and transferring the data to an excel file, obstacle detection is carried out with the best accuracy and speed using the Yolo algorithm version 3. This way, the network learns the response of the driver to obstacles in different locations and the network is trained with the Yolo algorithm version 3 and the output of obstacle detection. Then, it outputs the steer angle and amount of brake, gas, and vehicle acceleration. The LSV-DNN is evaluated here via extensive simulations carried out in Python and TensorFlow environment. We evaluated the network error using the loss function. By comparing other methods which were conducted on the simulator’s data, we obtained good performance results for the designed network on the data from KITTI benchmark, the data collected using a private vehicle, and the data we collected.

scholarly journals Inversion for Damping Ratio of Flat Blade Based on BP Neural Network

Proceedings ◽  
2018 ◽  
Vol 2 (8) ◽  
pp. 547
Author(s):  
Xiamei Zhang ◽  
Shudan Xia
Keyword(s):  
Neural Network ◽  
Decay Time ◽  
Bp Neural Network ◽  
Damping Ratio ◽  
Drop Test ◽  
Analysis Model ◽  
Bp Network ◽  
The Real ◽  
Neural Network Inversion ◽  
The Impact

Aero engine is impacted by foreign objects frequently during daily usage, including runway gravel, birds, fuselage components and so on, so the fan and compressor may damage, resulting in serious air crash. Thus, simulating the impact of blades and establishing the numerical analysis model of dynamic response demand immediate attention. In the analysis model, damping coefficient is one of the most important physical parameters of the blade structure and cannot be directly measured. Rayleigh damping is widely applied and can be converted to direct modal damping in ABAQUS. BP neural network is a multi-layer feedforward neural network using back propagation algorithm to adjust the network weights. It can be proved that there exists a three-layer BP network to realize the mapping of arbitrary continuous functions with arbitrary precision. In this study, a novel method for obtaining the damping ratio of the flat blade which applies BP neural network inversion is proposed. In order to demonstrate this method, a simplified experiment was conducted. Firstly, fix a section of aluminum plate and then conduct two set of drop tests on different positions with different impact velocities by a steel ball. At the same time, vibration response was recorded by displacement sensor. Secondly, establish a finite element model using ABAQUS to simulate the drop test. Adopt twenty groups of models with different damping ratio and then obtain their amplitudes and decay time, respectively. Thirdly, train a BP neural network using MATLAB program and then establish the mapping relationship between amplitude, decay time and damping ratio. Fourth, a set of experimental amplitude and decay time is substituted into the previously obtained BP neural network mapping model, and then the real damping ratio is obtained by inference. Finally, the real damping ratio is applied to the flat blade impact simulation of the other set of drop test for validation. The numerical results are consistent with the experimental data, which indicates that the damping ratio obtained by BP neural network inversion is reasonable and reliable.

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