Direction finding of bistatic MIMO radar in strong impulse noise

For bistatic multiple-input multiple-output(MIMO)radar,this paper presents a robust and direction finding method in strong impulse noise environment.By means of a new lower order covariance,the method is effective in suppressing impulse noise and achieving superior direction finding performance using the maximum likelihood(ML)estimation method.A quantum equilibrium optimizer algorithm(QEOA)is devised to resolve the corresponding objective function for efficient and accurate direc-tion finding.The results of simulation reveal the capability of the presented method in success rate and root mean square error over existing direction-finding methods in different application situations,e.g.,locating coherent signal sources with very few snapshots in strong impulse noise.Other than that,the Cramér-Rao bound(CRB)under impulse noise environment has been drawn to test the capability of the presented method.

3 - 10 GHz Ultra-Wideband Low-Noise Amplifier Using Inductive-Series Peaking Technique with Cascode Common-Source Circuit

The objective of this paper is to investigate a ultra-wideband (UWB) low noise amplifier (LNA) by utilizing a two-stage cascade circuit schematic associated with inductive-series peaking technique, which can improve the bandwidth in the 3-10 GHz microwave monolithic integrated circuit (MMIC). The proposed UWB LNA amplifier was implemented with both co-planer waveguide (CPW) layout and 0.15-μm GaAs D-mode pHEMT technology. Based on those technologies, this proposed UWB LNA with a chip size of 1.5 mm x 1.4 mm, obtained a flatness gain 3-dB bandwidth of 4 - 8 GHz, the constant gain of 4 dB, noise figure lower than 5 dB, and the return loss better than –8.5 dB. Based on our experimental results, the low noise amplifier using the inductive-series peaking technique can obtain a wider bandwidth, low power consumption and high flatness of gain in the 3 - 10 GHz. Finally, the overall LNA characterization exhibits ultra-wide bandwidth and low noise characterization, which illustrates that the proposed UWB LNA has a compact size and favorable RF characteristics. This UWB LNA circuit demonstrated the high RF characterization and could provide for the low noise micro-wave circuit applications.

An X-Band Low Noise Amplifier Design for Marine Navigation Radars

In this paper, the design of a 9.1 GHz Low Noise Amplifier (LNA) of a RADAR receiver that is used in the Navy is presented. For the design of the LNA, we used GaAs Field-Effect Transistors (FETs) from Agilent ADS component library. In order to keep the cost of the circuit in low prices and the performance high, we design a two-stage LNA.

Noise Temperature Characteristics and Gain-control of Avalanche Photodiodes for Laser Radar

Avalanche photodiodes(APDs) are promising light sensors with high quantum efficiency and low noise. It has been extensively used in radiation detection,laser radar and other weak signal detection fields. Unlike other photodiodes,APD is a very sensitive light detector with very high internal gain. The basic theory shows that the gain of APD is related to the temperature. The internal gain fluctuates with the variation of temperature. Investigated was the influence of the variation of the gain induced by the fluctuation of temperature on the output from APD for a very weak laser pulse input in laser radar. An active reverse-biased voltage compensation method is used to stabilize the gain of APD. An APD model is setup to simulate the detection of light pulse signal. The avalanche process,various noises and temperature's effect are all included in the model. Our results show that for the detection of weak light signal such as in laser radar,even a very small fluctuation of temperature could cause a great effect on APD's gain. The results show that the signal-to-noise ratio of the APD's output could be improved effectively with the active gain-control system.

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.

Research on weak signal extraction and noise removal for GPR data based on principal component analysis

The ground penetrating radar （GPR） detection data is a wide band signal, always disturbed by some noise, such as ambient random noise and muhiple refleetion waves. The noise affects the target identification of underground medium seriously. A method based on principal component analysis （PCA） was proposed to ex- tract the target signal and remove the uncorrelated noise. According to the correlation of signal, the authors get the eigenvalues and corresponding eigenvectors by decomposing the covariance matrix of GPR data and make linear transformation for the GPR data to get the principal components （PCs）. The lower-order PCs stand h^r the strong correlated target signals of the raw data, and the higher-order ones present the uneorrelated noise. Thus the authors can extract the target signal and filter uncorrelated noise effectively by the PCA. This method was demonstrated on real ultra-wideband through-wall radar data and simulated GPR data. Both of the results show that the PCA method can effectively extract the GPR target signal and remove the uncorrelated noise.

Human Motion Recognition Using Ultra-Wideband Radar and Cameras on Mobile Robot

Cameras can reliably detect human motions in a normal environment, but they are usually affected by sudden illumination changes and complex conditions, which are the major obstacles to the reliability and robustness of the system. To solve this problem, a novel integration method was proposed to combine hi-static ultra-wideband radar and cameras. In this recognition system, two cameras are used to localize the object＇s region, regions while a radar is used to obtain its 3D motion models on a mobile robot. The recognition results can be matched in the 3D motion library in order to recognize its motions. To confirm the effectiveness of the proposed method, the experimental results of recognition using vision sensors and those of recognition using the integration method were compared in different environments. Higher correct-recognition rate is achieved in the experiment.

INTERFEROGRAM NOISE REDUCTION ALGORITHM BASE ON MAXIMUM A POSTERIORI ESTIMATE

Interferogram noise reduction is a very important processing step in Interferometric Synthetic Aperture Radar(InSAR) technique. The most difficulty for this step is to remove the noises and preserve the fringes simultaneously. To solve the dilemma, a new interferogram noise reduction algorithm based on the Maximum A Posteriori(MAP) estimate is introduced in this paper. The algorithm is solved under the Total Generalized Variation(TGV) minimization assumption, which exploits the phase characteristics up to the second order differentiation. The ideal noise-free phase consisting of piecewise smooth areas is involved in this assumption, which is coincident with the natural terrain. In order to overcome the phase wraparound effect, complex plane filter is utilized in this algorithm. The simulation and real data experiments show the algorithm can reduce the noises effectively and meanwhile preserve the interferogram fringes very well.

Inductorless Ultra-Wide Band Front-End Chip Design with Noise Cancellation Technology for Wireless Communication Applications 10

An inductorless Ultra-Wide Band （UWB） receiver frontend chip design used in wireless communications for the frequency band of 3.1 - 4.8 GHz is presented. This ho-nodyne receiver mainly consists of a diffexential Low Noise Amplifier （LNA） circuit followed by a down-converting mixer. The proposed LNA circuit with a noise canceling resistor is connected to the CMOS device＇s body to reduce the substrate thermal noise. Simulation and measuremnt results show that the chip can reduce the froat-end Noise Figure （NF） about 0.5dB and achieve the Conversion Gain （03） of 19.44-21.57 dB and double-sideband NF less than 7.8 dB. Also, the input third-order interoept point （IIP3） is - 11 dBm, and the input second-order intercept point （IIP2） is 49 dBm. Fabricated in TSMC 0.18 tan technology, this chip occupies only 0. 167 Iron2 and dissipates power 59.2 roW.

A 9 - 10.6 GHz Microstrip Antenna—UWB Low Noise Amplifier with Differential Noise Canceling Technique for IoT Applications

An ultra-wide band (UWB) receiver front-end that operates at the UWB frequency range, starting from 9 GHz - 10.6 GHz is proposed in this paper. The proposed system consists of an off-chip microstrip antenna and CMOS differential low noise amplifier with a differential noise canceling (DNC) technique. The proposed antenna is trapezoidal dipole shaped with balun and printed on a low-cost FR4 substrate with dimensions 10 × 10 × 0.8 mm3. The balun circuit integrated with the ground antenna to improve the antenna impedance matching. Noise canceling is obtained by using a differential block with each stage having 2 amplifiers that generate differential signals, subtracted to improve total noise performance. The proposed DNC block improves NF by 50% while increasing total power consumption with only 0.1 Mw. The differential CMOS cascode LNA with DNC block is implemented using UMC 0.13 μm CMOS process, exhibits a flat gain of 19 dB, maximum noise figure of 2.75 dB, 1 dB compression point −16 dBm and 3rd order intercept point (IIP3) −10 dBm. The proposed system has total DC power consumption of 2.8 mW at 1.2 V power supply.

Improving the Autoregressive Modeling Method in Random Noise Suppression of GPR Data Using Undecimated Discrete Wavelet Transform

Geophysics has played a significant and efficient role in studying geological structures over the past decades as the goal of geophysical data acquisition is to investigate underground phenomena with the highest possible level of accuracy. The ground penetrating radar (GPR) method is used as a nondestructive method to reveal shallow structures by beaming electromagnetic waves through the Earth and recording the received reflections, albeit inevitably, along with random noise. Various types of noise affect GPR data, among the most important of which are random noise resulting from arbitrary motions of particles during data acquisition. Random noise which exists always and at all frequencies, along with coherent noise, reduces the quality of GPR data and must be reduced as much as possible. Over the recent years, discrete wavelet transform has proved to be an efficient tool in signal processing, especially in image and signal compressing and noise suppression. It also allows for obtaining an accurate understanding of the signal properties. In this study, we have used the autoregression in both wavelet and f-x domains to suppress random noise in synthetic and real GPR data. Finally, we compare noise suppression in the two domains. Our results reveal that noise suppression is conducted more efficiently in the wavelet domain due to decomposing the signal into separate subbands and exclusively applying the method parameters in autoregression modeling for each subband.

NOISE AMPLIFICATION ANALYSIS AND COMPARISON OF TWO PERIODIC NONUNIFORM SAMPLING RECONSTRUCTION METHODS USED IN DPCA SAR

General Sampling Expansion Reconstruction Method (GSERM) and Digital Spectrum Reconstruction Method (DSRM), which prove effective to reconstruct azimuth signal of Displaced Phase Center Apertures (DPCA) Synthetic Aperture Radar (SAR) system from its Periodic Non-Uniform Sampling (PNUS) data sequences, would amplify the noise and sidelobe clutter simultaneously in the reconstruction. This paper formulates the relation of the system transfer matrixes of the above two methods, gives the properties, such as periodicity, symmetry, and time-shift property, of their Noise and Sidelobe Clutter Amplification Factor (NSCAF), and discovers that DSRM is more sensitive than GSERM in the white noise environment. In addition, criteria based on initial sampling point analysis for the robust PRF selection are suggested. Computer simulation results support these con-clusions.

Adaptive waveform design based on Morlet wavelet for ultra-wideband MIMO radar

For the issue of deterioration in detection performance caused by dynamically changing environment in ultra-wideband（UWB） multiple input multiple output（MIMO） radar, this paper proposes a novel adaptive waveform design which is aimed to improve the ability of discriminating target and clutter from the radar scene. Firstly, a sequence of Morlet wavelet pulses with frequency hopping and pulse position modulation by Welch-Costas array is designed. Then a waveform optimization solution is proposed which is achieved by applying the minimization mutual-information（MI） strategy. After that, with subsequent iterations of the algorithm, simulation results demonstrate that the optimal waveform design method brings an improvement in the target detection ability in the presence of noise and clutter.

基于噪声卷积调制的SAR多区域灵巧干扰方法

针对传统噪声卷积干扰存在的压制区域位置在方位向上不能灵活控制的缺陷,提出一种基于噪声卷积调制的SAR多区域灵巧干扰方法。该方法对截获信号进行距离向移频、方位向移相以及与噪声模板的卷积调制处理,以控制压制区域的位置;后对截获信号进行不同频率、相位的噪声卷积调制处理,以控制压制区域的数量。理论和仿真结果表明,所提方法实现了压制区域位置在方位向、距离向上的灵活控制,并实现了单一干扰机对多个待掩护区域的干扰,提高了干扰增益,干扰效果较好,对实际工程应用具有一定的参考价值。

基于模糊神经网络的舰船雷达图像弱小目标检测

舰船雷达图像信息的维度较高,导致弱小目标的关键特征难以被精准提取,降低了弱小目标检测的可靠性,因此提出一种基于模糊神经网络的舰船雷达图像弱小目标检测方法。该方法对舰船雷达图像进行背景校正,利用图像灰度值加性模型从图像中提取弱小目标。最后将提取的弱小目标输入到模糊神经网络中,输出的结果即为舰船雷达图像弱小目标检测结果。通过实验证明,在不同高斯噪声环境中,该方法能够准确地检测出雷达图像中的弱小目标,并具有较快的检测速度。

空域色噪声下的多输入多输出雷达角度估计

由于多输入多输出(multiple input multiple output,MIMO)雷达的空域色噪声协方差矩阵通常为非对角矩阵,因此在色噪声下信号子空间与噪声子空间无法有效分离,从而致使传统算法无法有效估计目标角度。为此,首先利用信号协方差矩阵的低秩性和色噪声协方差矩阵的稀疏性来抑制空域色噪声。然后,根据MIMO雷达数据的内在多维结构特性,建立四阶张量CP(canonical or parallel factor analysis,CANDECOMP/PARAFAC)分解模型。针对传统交替最小二乘算法对数值病态性较为敏感而导致CP分解精度低的问题,利用张量因子矩阵之间的共轭关系来降低求解的病态敏感度,提高张量分解的稳健性。最后,利用最小二乘拟合法从因子矩阵的估计值中得到目标角度。仿真结果表明,所提算法能够对色噪声有效抑制并提高了角度估计的精度。

GPR信号去噪的变分模态分解

为了进一步提高探地雷达(ground penetrating radar,GPR)数据的信噪比,压制由随机扰动引起的随机绕射能量,将二维变分模态分解(two-dimensional variational mode decomposition,2D-VMD)引入二维GPR数据的噪声压制处理中。首先,对GPR数据进行2D-VMD处理,并分析各阶本征模态函数(intrinsic mode function,IMF)分量及其对应的频率-波数域谱来确定雷达剖面中的各回波类型。然后,计算IMF分量与原始数据的互相关系数来确定信号模态和噪声模态,并对信号模态进行重构得到降噪后的数据。理论数据和实测数据测试表明,相比于传统的1D-VMD法,2D-VMD滤波后的含噪正演记录峰值信噪比由6.44 dB增加到7.72 dB;经2D-VMD降噪处理后的雷达剖面在保留有效信号的基础上,可以有效压制随机扰动带来的噪声,并且得到的雷达剖面同相轴连续性更好。

基于多维小波特征的有源雷达欺骗干扰识别

针对跟踪工作模式下常见雷达有源欺骗干扰识别问题,通过分析雷达信号和干扰模型,提出一种基于多维小波特征的识别算法。对目标回波多个相干处理间隔中的首个脉冲重复间隔内信号采样累积,构成一维离散序列,利用Mallat算法提取多尺度小波系数,计算不同尺度下小波系数的移位相关系数,并组成特征向量用于分类识别。构建不同参数的信号样本仿真,在信噪比为-5 dB时仍有高于80%的识别正确率。该算法能将距离假目标干扰和距离拖引干扰有效识别,证明了算法的准确性、通用性和严谨性。此外,针对实际情况,可以调整特征向量维度,选择更适合的母小波函数,进一步优化干扰识别效果。

基于纯自注意力机制的毫米波雷达手势识别

在构建智慧控制,万物互联的背景下,通过手势远程控制设备,进行人机交互逐渐成为研究热点。对此,提出了一种以毫米波雷达为传感器,采用基于纯自注意力机制模型实现手势识别的方法。首先,采集正面视角的13类手势的时序回波数据。接着,对数据进行三维快速傅里叶变换(three-dimension fast Fourier transform,3D-FFT)、动目标显示(moving target indication,MTI)、恒虚警率(constant false alarm rate,CFAR)检测操作并进行固定种类特征提取,将这些特征传入基于纯自注意力机制网络的雷达特征变换(radar feature transformer,RFT)网络。最后,基于实测数据完成了数据特征提取、网络训练、手势识别等步骤。实验结果表明,所提方法在测试集上准确率达到95.38%,网络训练时间短,模型复杂度低,泛化性好,为现有研究提供了新的研究思路。

一种基于改进NLMS的多雷达主瓣干扰抑制方法

远程探测场景下弹载主瓣干扰因与目标角度间隔小,单站抗干扰方法性能下降严重。多雷达通过合理布站可有效“分辨”目标和干扰,被认为是可有效抑制主瓣干扰的途径。针对当前多雷达抗主瓣干扰方法普遍存在收敛速度慢的问题,提出一种基于改进归一化最小均方(NLMS)的多雷达对消抗主瓣干扰方法,并通过理论推导给出了其应用条件。该方法通过定义消散因子实现对步长的自适应控制从而达到快速收敛的目的,仿真结果表明该方法在收敛速度及收敛后均方误差(MSE)上具有明显优势。最后通过外场试验进一步验证该方法的性能,在满足布站条件且干噪比≥30 dB的前提下,信噪比改善可达19 dB以上,具有较高的应用价值。