HQNN-SFOP:Hybrid Quantum Neural Networks with Signal Feature Overlay Projection for Drone Detection Using Radar Return Signals-A Simulation

With the wide application of drone technology,there is an increasing demand for the detection of radar return signals from drones.Existing detection methods mainly rely on time-frequency domain feature extraction and classical machine learning algorithms for image recognition.This method suffers from the problem of large dimensionality of image features,which leads to large input data size and noise affecting learning.Therefore,this paper proposes to extract signal time-domain statistical features for radar return signals from drones and reduce the feature dimension from 512×4 to 16 dimensions.However,the downscaled feature data makes the accuracy of traditional machine learning algorithms decrease,so we propose a new hybrid quantum neural network with signal feature overlay projection(HQNN-SFOP),which reduces the dimensionality of the signal by extracting the statistical features in the time domain of the signal,introduces the signal feature overlay projection to enhance the expression ability of quantum computation on the signal features,and introduces the quantum circuits to improve the neural network’s ability to obtain the inline relationship of features,thus improving the accuracy and migration generalization ability of drone detection.In order to validate the effectiveness of the proposed method,we experimented with the method using the MM model that combines the real parameters of five commercial drones and random drones parameters to generate data to simulate a realistic environment.The results show that the method based on statistical features in the time domain of the signal is able to extract features at smaller scales and obtain higher accuracy on a dataset with an SNR of 10 dB.On the time-domain feature data set,HQNNSFOP obtains the highest accuracy compared to other conventional methods.In addition,HQNN-SFOP has good migration generalization ability on five commercial drones and random drones data at different SNR conditions.Our method verifies the feasibility and effectiveness of signal detection methods based on quantum computation and experimentally demonstrates that the advantages of quantum computation for information processing are still valid in the field of signal processing,it provides a highly efficient method for the drone detection using radar return signals.

Equalization Reconstruction Algorithm Based on Reference Signal Frequency Domain Block Joint for DTMB-Based Passive Radar

Channel equalization plays a pivotal role within the reconstruction phase of passive radar reference signals.In the context of reconstructing digital terrestrial multimedia broadcasting(DTMB)signals for low-slow-small(LSS)target detection,a novel frequency domain block joint equalization algorithm is presented in this article.From the DTMB signal frame structure and channel multipath transmission characteristics,this article adopts a unconventional approach where the delay and frame structure of each DTMB signal frame are reconfigured to create a circular convolution block,facilitating concurrent fast Fourier transform(FFT)calculations.Following equalization,an inverse fast Fourier transform(IFFT)-based joint output and subsequent data reordering are executed to finalize the equalization process for the DTMB signal.Simulation and measured data confirm that this algorithm outperforms conventional techniques by reducing signal errors rate and enhancing real-time processing.In passive radar LSS detection,it effectively suppresses multipath and noise through frequency domain equalization,reducing false alarms and improving the capabilities of weak target detection.

Detection method of forward-scatter signal based on Rényi entropy

The application scope of the forward scatter radar(FSR)based on the Global Navigation Satellite System(GNSS)can be expanded by improving the detection capability.Firstly,the forward-scatter signal model when the target crosses the baseline is constructed.Then,the detection method of the for-ward-scatter signal based on the Rényi entropy of time-fre-quency distribution is proposed and the detection performance with different time-frequency distributions is compared.Simula-tion results show that the method based on the smooth pseudo Wigner-Ville distribution(SPWVD)can achieve the best perfor-mance.Next,combined with the geometry of FSR,the influence on detection performance of the relative distance between the target and the baseline is analyzed.Finally,the proposed method is validated by the anechoic chamber measurements and the results show that the detection ability has a 10 dB improvement compared with the common constant false alarm rate(CFAR)detection.

Coarse-fine joint target parameter estimation method based on AN-RSC in OFDM passive radar

In this paper,we study the accuracy of delay-Doppler parameter estimation of targets in a passive radar using orthogonal frequency division multiplexing(OFDM)signal.A coarse-fine joint estimation method is proposed to achieve better estimation accuracy of target parameters without excessive computational burden.Firstly,the modulation symbol domain(MSD)method is used to roughly estimate the delay and Doppler of targets.Then,to obtain high-precision Doppler estimation,the atomic norm(AN)based on the multiple measurement vectors(MMV)model(MMV-AN)is used to manifest the signal sparsity in the continuous Doppler domain.At the same time,a reference signal compensation(RSC)method is presented to obtain highprecision delay estimation.Simulation results based on the OFDM signal show that the coarse-fine joint estimation method based on AN-RSC can obtain a more accurate estimation of target parameters compared with other algorithms.In addition,the proposed method also possesses computational advantages compared with the joint parameter estimation.

Signal and Data Processing of Television Based on Multistatic Radar Systems

A television based multistatic radar system is described. The commercial television transmitter is used as the illuminator in the multistatic radar system. The reflected commercial television signals are measured by an array of sensors. A data processing scheme is developed that adapts to the poor signal processing ability. The innovation is focused on the construction of the observation space, which could reduce the non linearity error. The new method leads to better system stability than the traditional one. Monte Carlo simulation is utilized and compared with the traditional method.

Multi-Component Signal Processing for the Short Range Radar

To study the measurement of distance under the condition of the frequency modulation (FM) multi component signal of a short range radar, the multi points scattering model of a target, the TLS ESPRIT (total least square estimation of signal parameters via rotational invariance techniques) and the mathematical statistics methods were used. The method of computing single frequency signal's instantaneous frequency (IF) is unsuitable to the multi component signal. By using the method of the TLS ESPRIT combined with the mathematical statistics, the multi component signal's IF can be obtained. The computer simulation has shown that the method has the high accuracy for measuring the distance.

Radar Emitter Signal Recognition Based on Complexity Features

Intra-pulse characteristics of different radar emitter signals reflect on signal waveform by way of changing frequency, phase and amplitude. A novel approach was proposed to extract complexity features of radar emitter signals in a wide range of signal-to-noise ratio (SNR), and radial basis probability neural network (RBPNN) was used to recognize different radar emitter signals. Complexity features, including Lempel-Ziv complexity (LZC) and correlation dimension (CD), can measure the complexity and irregularity of signals, which mirrors the intra-pulse modulation laws of radar emitter signals. In an experiment, LZC and CD features of 10 typical radar emitter signals were extracted and RBPNN was applied to identify the 10 radar emitter signals. Simulation results show that the proposed approach is effective and has good application values because average accurate recognition rate is high when SNR varies in a wide range.

Signal classification method based on data mining formulti-mode radar

For the multi-mode radar working in the modern electronicbattlefield, different working states of one single radar areprone to being classified as multiple emitters when adoptingtraditional classification methods to process intercepted signals,which has a negative effect on signal classification. A classificationmethod based on spatial data mining is presented to address theabove challenge. Inspired by the idea of spatial data mining, theclassification method applies nuclear field to depicting the distributioninformation of pulse samples in feature space, and digs out thehidden cluster information by analyzing distribution characteristics.In addition, a membership-degree criterion to quantify the correlationamong all classes is established, which ensures classificationaccuracy of signal samples. Numerical experiments show that thepresented method can effectively prevent different working statesof multi-mode emitter from being classified as several emitters,and achieve higher classification accuracy.

Polyphase coded signal design for MIMO radar using MO-MicPSO

A novel modified optimization technique known as the multi-objective micro particle swarm optimization（MO-MicPSO） is proposed for polyphase coded signal design.The proposed MO-MicPSO requires only a small population size compared with the standard particle swarm optimization that uses a larger population size.This new method is guided by an elite archive to finish the multi-objective optimization.The orthogonal polyphase coded signal（OPCS） can fundamentally improve the multiple input multiple output（MIMO） radar system performance,with which the radar system has high resolution and abundant signal channels.Simulation results on the polyphase coded signal design show that the MO-MicPSO can perform quite well for this high-dimensional multi-objective optimized problem.Compared with particle swarm optimization or genetic algorithm,the proposed MO-MicPSO has a better optimized efficiency and less time consumption.

Feature Extraction and Classification of Echo Signal of Ground Penetrating Radar

Automatic feature extraction and classification algorithm of echo signal of ground penetrating radar is presented. Dyadic wavelet transform and the average energy of the wavelet coefficients are applied in this paper to decompose and extract feature of the echo signal. Then, the extracted feature vector is fed up to a feed forward muhi layer perceptron classifier. Experimental results based on the measured GPR, echo signals obtained from the Mei shan railway are presented.

Radar Emitter Signal Recognition Using Wavelet Packet Transform and Support Vector Machines

This paper presents a novel method for radar emitter signal recognition. First, wavelet packet transform （WPT） is introduced to extract features from radar emitter signals. Then, rough set theory is used to select the optimal feature subset with good discriminability from original feature set, and support vector machines （SVMs） are employed to design classifiers. A large number of experimental results show that the proposed method achieves very high recognition rates for 9 radar emitter signals in a wide range of signal-to-noise rates, and proves a feasible and valid method.

Human Respiration Rate Estimation Using Ultra-wideband Distributed Cognitive Radar System

It has been shown that remote monitoring of pulmonary activity can be achieved using ultra-wideband (UWB) systems, which shows promise in home healthcare,rescue,and security applications.In this paper,we first present a multi-ray propagation model for UWB signal,which is traveling through the human thorax and is reflected on the air/dry-skin/fat/muscle interfaces,A geometry-based statistical channel model is then developed for simulating the reception of UWB signals in the indoor propagation environment.This model enables replication of time-varying multipath profiles due to the displacement of a human chest.Subsequently, a UWB distributed cognitive radar system (UWB-DCRS) is developed for the robust detection of chest cavity motion and the accurate estimation of respiration rate.The analytical framework can serve as a basis in the planning and evaluation of future rheasurement programs.We also provide a case study on how the antenna beamwidth affects the estimation of respiration rate based on the proposed propagation models and system architecture.

Radar Signal Recognition by CWD Picture Features

In this paper a system for automatic recognition of radar waveform is introduced. This technique is used in many spectrum management, surveillance, and cognitive radio and radar applications. For instance the transmitted radar signal is coded into six codes based on pulse compression waveform such as linear frequency modulation (LFM), Frank code, P1, P2, P3 and P4 codes, the latter four are poly phase codes. The classification system is based on drawing Choi Willliams Distribution (CWD) picture and extracting features from it. In this study, various new types of features are extracted from CWD picture and then a pattern recognition method is used to recognize the spectrum. In fact, signals from CWD picture are defined using biometric techniques. We also employ false reject rate (FRR) and false accept rate (FAR) which are two types of fault measurement criteria that are deploy in biometric papers. Fairly good results are obtained for recognition of Signal to Noise Ratio (-11 dB).

Single channel source separation of radar fuze mixed signal based on phase difference analysis

A new method based on phase difference analysis is proposed for the single-channel mixed signal separation of single-channel radar fuze.This method is used to estimate the mixing coefficients of de-noised signals through the cumulants of mixed signals,solve the candidate data set by the mixing coefficients and signal analytical form,and resolve the problem of vector ambiguity by analyzing the phase differences.The signal separation is realized by exchanging data of the solutions.The waveform similarity coefficients are calculated,and the time鈥攆requency distributions of separated signals are analyzed.The results show that the proposed method is effective.

Modulation recognition of MIMO radar signal based on joint HOS and SNR algorithm

This paper presents a joint high order statistics （HOS） and signal-to-noise ratio （SNR） algorithm for the recognition of multiple-input multiple-output （MIMO） radar signal without a priori knowledge of the signal parameters. This method is capable of recognizing the MIMO radar signal as well as discriminating it from single-carrier signal adopted by conventional radar. Meanwhile, the sub-carrier number of the none-coding MIMO radar signal is estimated. Extensive simulations are carried out in different operating conditions. Simulation results prove the feasibility and indicate that the recognition probability could reach over 90% when the value of SNR is above 0 dB.

High Resolution Radar Real-Time Signal and Information Processing

Radar is an electronic device that uses radio waves to determine the range, angle, or velocity of objects. Real-time signal and information processor is an important module for real-time positioning, imaging, detection and recognition of targets. With the development of ultra-wideband technology, synthetic aperture technology, signal and information processing technology, the radar coverage, detection accuracy and resolution have been greatly improved, especially in terms of one-dimensional(1D) high-resolution radar detection, tracking, recognition, and two-dimensional(2D) synthetic aperture radar imaging technology. Meanwhile, for the application of radar detection and remote sensing with high resolution and wide swath, the amount of data has been greatly increased. Therefore, the radar is required to have low-latency and real-time processing capability under the constraints of size, weight and power consumption. This paper systematically introduces the new technology of high resolution radar and real-time signal and information processing. The key problems and solutions are discussed, including the detection and tracking of 1D high-resolution radar, the accurate signal modeling and wide-swath imaging for geosynchronous orbit synthetic aperture radar, and real-time signal and information processing architecture and efficient algorithms. Finally, the latest research progress and representative results are presented, and the development trends are prospected.

Synthetic Aperture Radar Raw Signals Simulation of Extended Scenes

A synthetic aperture radar (SAR) raw signal simulation algorithm for extended scenes is presented. This algorithm is based on the SAR two-dimensional system transform function (STF). To cope with range-variant nature of SAR STF and increase the speed of this algorithms, new formulas for range-variant phase corrections in range-Doppler (RD) domain are developed. In this way, many azimuth lines can be simulated with the same SAR STF. It only needs two-dimensional fast Fourier transform code and complex multiplications. Comparing with time-domain simulation algorithm, it is very simple and thus efficient. Simulation results have shown that this algorithm is accurate and efficient. Key words synthetic aperture radar - raw signal simulation - system transform function CLC number TP 751. 1 Foundation item: Supported by the National Natural Science Foundation of China (40376051)Biography: Sun Jin-yao (1967-), female, Ph. D. candidate, research direction: SAR image simulation and 3D recover for SAR image.

ULTRAWIDE BAND RADAR SIGNAL DETECTION, ESTIMATION AND EXPERIMENT

With reference to the air target detection of ultra-wide band (UWB)/impulse radar(IR), the transient signal processing techniques was discussed. In weak UWB signal detection, the wavelet transforms and high order spectrum estimation techniques were preferred. In target characteristic analysis, a time domain bispectrum estimation algorithm was used to analyze the target impulse response, which could estimate accurately local scattering distribution of complex target. A free field IR experimental system installed in an anechoic chamber was used. With this system, experiments to several target models were made. The results of these experiments verified the signal processing method efficiency.

Kravchenko probabilityweight functions in problems of radar signals correlation processing

In this report the combined method of correlation radar signal(RS)processing based on the theory of atomic functions(AF)is examined.Examples of using of new Kravchenko probability weight functions(WF)designs are presented.Quality functional to estimate accuracy and efficiency of RS processing for concrete physical models is constructed.It is shown that the proposed approach significantly improves the quality of the coherent analysis of RS.

Design of a Radar Signal Simulator Based on Virtex-Ⅱ Series FPGA

A simulation method to simulate the pseudorandom code P. M PP radar＇ s echo signal is proposed that makes use of the pre-generated Doppler simulation data, according to the relative movement parameter of the radar and the target. It resolves the problem of the high precision distance simulation and the high speed digital shift phase. At the same time, the radar dynamic digital video frequency target signal simulator is designed. Simulation results of the critical unit and the output waveform are given. The result of the test satisfies the system＇s request.