Robust adaptive radar beamforming based on iterative training sample selection using kurtosis of generalized inner product statistics

In engineering application,there is only one adaptive weights estimated by most of traditional early warning radars for adaptive interference suppression in a pulse reputation interval(PRI).Therefore,if the training samples used to calculate the weight vector does not contain the jamming,then the jamming cannot be removed by adaptive spatial filtering.If the weight vector is constantly updated in the range dimension,the training data may contain target echo signals,resulting in signal cancellation effect.To cope with the situation that the training samples are contaminated by target signal,an iterative training sample selection method based on non-homogeneous detector(NHD)is proposed in this paper for updating the weight vector in entire range dimension.The principle is presented,and the validity is proven by simulation results.

RLS and LMS blind adaptive multi-user detection method and comparison in acoustic communication

RLS and LMS blind adaptive multi-user detection algorithm and multi-user detector was proposed to solve the problem of multi-user signal detection problem encountered in underwater acoustic communication networks.In simulation analysis,RLS and the LMS blind adaptive multi-user detector were designed and tested for synchronous and asynchronous multi-user communication process.The results of SIR comparison and MMSE comparison show that,both of the two methods can realize blind adaptive detection when any user change in multi-user communication,during this process,the training communication sequences are not needed.The RLS algorithm has about 5 dB higher in SIR compared with LMS algorithm,and the convergence velocity of RLS algorithm is also higher than LMS algorithm when the communication users change.RLS algorithm has better ability in multi-user detection than that of LMS algorithm,and it has great attraction and guiding significance for solving the problem of multiple access interference(MAI) in multi-user communication.

A Novel Radius Adaptive Based on Center-Optimized Hybrid Detector Generation Algorithm

Negative selection algorithm(NSA)is one of the classic artificial immune algorithm widely used in anomaly detection.However,there are still unsolved shortcomings of NSA that limit its further applications.For example,the nonselfdetector generation efficiency is low;a large number of nonselfdetector is needed for precise detection;low detection rate with various application data sets.Aiming at those problems,a novel radius adaptive based on center-optimized hybrid detector generation algorithm(RACO-HDG)is put forward.To our best knowledge,radius adaptive based on center optimization is first time analyzed and proposed as an efficient mechanism to improve both detector generation and detection rate without significant computation complexity.RACO-HDG works efficiently in three phases.At first,a small number of self-detectors are generated,different from typical NSAs with a large number of self-sample are generated.Nonself-detectors will be generated from those initial small number of self-detectors to make hybrid detection of self-detectors and nonself-detectors possible.Secondly,without any prior knowledge of the data sets or manual setting,the nonself-detector radius threshold is self-adaptive by optimizing the nonself-detector center and the generation mechanism.In this way,the number of abnormal detectors is decreased sharply,while the coverage area of the nonself-detector is increased otherwise,leading to higher detection performances of RACOHDG.Finally,hybrid detection algorithm is proposed with both self-detectors and nonself-detectors work together to increase detection rate as expected.Abundant simulations and application results show that the proposed RACO-HDG has higher detection rate,lower false alarm rate and higher detection efficiency compared with other excellent algorithms.

A Novel Stochastic Blind Adaptive Multiuser Detector for CDMA Systems

Blind adaptive multiuser detector has become a research hotspot in recent years due to a number of advantages, but many blind adaptive algorithms involve low convergence rate. This paper presents a novel stochastic blind adaptive multiuser detector without requiring training sequences, which needs only two system parameters: the signature sequence of the desired user i, s i and the variance of the additive white Gaussian noise (AWGN),σ 2. Simulation results show that by reasonably choosing time varying step size, the proposed detector can not only improve the convergence rate, but also reduce the limiting NSE (Normalized Squared Error) values, so it can effectively increase the performance of the system.

A Neuro-inspired Adaptive Motion Detector

In this paper, a novel motion detector is proposed to perceive the weak changes in a image sequence. This is inspired by the mechanism of fixational eye movement and dynamics of vertebrate’s cortex. We realized respectively an artificial model of visual attention selection, called dual-probe adaptive model (DPAM), and an active tremor operation (ATO) approach. It is found that between them there exists a resonance phenomenon. The phenomenon is enhanced when the ATO and the DPAM are in-phase and is suppressed when they are anti-phase.?Based on this, we construct a novel motion detector combined by the ATO and the DPAM to resonate with the motion direction. This allows capturing moving edges even in the image sequences with lighting change and noisy background. Simulation and Experimental results demonstrate the effectiveness.

Adaptive detection in the presence of signal mismatch

The problem of adaptive detection in the situation of signal mismatch is considered; that is, the actual signal steering vector is not aligned with the nominal one. Two novel tunable detectors are proposed. They can control the degree to which the mismatched signals are rejected. Remarkably, it is found that they both cover existing famous detectors as their special cases. More importantly, they possess the constant false alarm rate（CFAR）property and achieve enhanced mismatched signal rejection or improved robustness than their natural competitors. Besides, they can provide slightly better matched signals detection performance than the existing detectors.

Research on monopulse forward-looking high-resolution imaging algorithm based on adaptive iteration

In this paper,we proposed a monopulse forward-looking high-resolution imaging algorithm based on adaptive iteration for missile-borne detector.Through iteration,the proposed algorithm automatically selects the echo signal of isolated strong-scattering points from the receiving echo signal data to accurately estimate the actual optimal monopulse response curve(MRC) of the same distance range,and we applied optimal MRC to realize the azimuth self-focusing in the process of imaging.We use real-time echo data to perform error correction for obtaining the optimal MRC,and the azimuth angulation accuracy may reach the optimum at a certain distance dimension.We experimentally demonstrate the validity,reliability and high performance of the proposed algorithm.The azimuth angulation accuracy may reach up to ten times of the detection beam-width.The simulation experiments have verified the feasibility of this strategy,with the average height measurement error being 7.8%.In the out-field unmanned aerial vehicle(UAV) tests,the height measurement error is less than 25 m,and the whole response time can satisfy the requirements of a missile-borne detector.

New blind adaptive approach to multiuser detection in CDMA systems

Due to a number of advantages, blind adaptive multiuser detector has become a research hotspot in recent years. But low convergence rate problem occurs to many blind adaptive algorithms. A new blind adaptive approach to multiuser detection is presented. The simulation results show that by reasonably choosing time varying step size, the proposed detector can not only improve the convergence rate, but also reduce the bit error rate (BER) of the system, and so it can effectively improve the system performance with less computational cost.

多尺度增强特征融合的钢表面缺陷目标检测

针对轻量级目标检测算法在钢表面缺陷检测任务中识别精度低的问题,提出一种多尺度增强特征融合的钢表面缺陷目标检测算法。该算法采用提出的自适应加权融合模块为不同层级特征自适应计算融合权重,将深层语义与浅层细节进行加权融合,使得浅层特征在不丢失细节信息的同时获得丰富的深层语义。利用提出的空间特征增强模块从3个独立方向强化融合特征,通过引出残差旁路增强网络结构的稳定性,使卷积过程能够挖掘到更多的关键信息。根据先验框与真实框的整体交并程度为模型选择更为合适的训练样本。实验结果表明,该算法的检测精度达到80.47%,相比原始算法提升6.81%。该算法的参数量为2.36 M,计算量为952.67 MFLOPs,能快速且高精度检测钢材表面的缺陷信息,具有较高的应用价值。

基于P-U-net的角锥波前探测器的波前校正方法

自适应光学技术被广泛应用于像差的校正,但在实际应用中,闭环校正需要较长的时间,并且由于变形镜的非线性响应,传统的开环校正方法的校正精度会受到影响。本文提出一种基于P-U-net网络的角锥波前探测器波前校正方法。P-U-net通过数据训练建立了整个自适应光学系统的模型,在角锥波前探测器的光瞳图像与变形镜的控制电压之间构建了直接的非线性映射关系。可以通过角锥波前探测器的光瞳图像直接计算变形镜的控制电压。通过使用仿真自适应系统理论验证了该方法的可行性,可以在50 ms内完成像差校正,校正精度达到0.1μm。与传统的闭环自适应校正算法相比,本文方法可以在更短的时间内完成像差校正,达到更高的精度,具有很好的应用前景。

CMOS探测器自适应信号读出技术研究

设计了一种根据光照强度自动调节增益的CMOS探测器,它能够在一帧时间内实现光照强度与积分增益自动适应,从而实现无论在弱光还是强光条件下CMOS探测器都能有适应的灵敏度和动态范围。相较于传统CTIA电路,自适应信号读出技术新增了比较器电路来控制CTIA积分电容大小,通过短曝光输出电压与参考阈值进行比较,输出信号结果用来调整长曝光积分增益,最终得到每个像素的输出电压和增益挡位。基于0.5μm 5 V-CMOS工艺进行了128×1线阵CMOS探测器设计仿真与流片,仿真结果表明,光电流在2 pA~100 nA六个数量级内分别自适应四个积分增益,都能有良好的信号读出。

基于自适应加权增强总变差的CT偏置扫描重建算法

基于降低设备制造成本或辐射剂量等目的,计算机断层成像(Computer Tomography,CT)中的一个实际需求是以有限的探测器尺寸来获得更大的视野(Field of View,FOV),通过将探测器放置在横向偏移位置可以有效的扩大FOV。然而,常规的重建算法无法精确重建偏置投影数据,针对这一问题,本文提出了一种基于自适应加权增强总变差最小化的偏置重建模型及CP(Chambolle-Pock)求解算法。具体来说,构建自适应加权增强总变差范数作为正则项,其中自适应权重根据局部增强梯度自适应调整权值,进而设计了一种基于自适应加权增强总变差最小化的偏置重建模型(Weighted Adaptive-weight reinforced Total Variation,WAwrTV),并推导出了相应的CP算法。实验结果表明,所提算法能有效的重建偏置投影数据并提高重建精度,且具有良好的抗噪性能。

A new detector in EBPSK communication system

In order to raise the detection precision of the extended binary phase shift keying （EBPSK） receiver, a detector based on the improved particle swarm optimization algorithm （IMPSO） and the BP neural network is designed. First, the characteristics of EBPSK modulated signals and the special filtering mechanism of the impacting filter are demonstrated. Secondly, an improved particle swarm optimization algorithm based on the logistic chaos disturbance operator and the Cauchy mutation operator is proposed, and the EBPSK detector is designed by utilizing the IMPSO-BP neural network. Finally, the simulation of the EBPSK detector based on the MPSO-BP neural network is conducted and the result is compared with that of the adaptive threshold-based decision, the BP neural network, and the PSO-BP detector, respectively. Simulation results show that the detection performance of the EBPSK detector based on the IMPSO-BP neural network is better than those of the other three detectors.

The Applicative Investigation of Adaptive BP Networks for Multi-user Detection in Asynchronous DS-CDMA Mobile Communications

Three-layer Adaptive Back-Propagation Neural Networks(TABPNN) are employed for the demodulation of spread spectrum signals in a multiple-access environment. A configuration employing three-layer adaptive Back-propagation neural networks is put forward for the demodulation of spread-spectrum signals in asynchronous Gaussian channels. The theoretical arguments and practical performance based on the neural networks are analyzed. The results show that whether the resistance to the multiple access interference or the robust to near-far effects, the proposed detector significantly outperforms not only the conventional detector but also the BP neural networks detector and is comparable to the optimum detector.

ANALYSIS OF CELL-AVERAGING BASED DETECTORS FOR χ^2 FLUCTUATING TARGETS IN MULTITARGET ENVIRONMENTS

The χ^2 family of signal fluctuation distributions represents the main fluctuation models which most radar targets follow it in their reflections. This family can be categorized as fluctuation distribution with two degrees of freedom and those with four degrees of freedom. The first category represents all important class of fluctuation models which when illuminated by a coherent pulse train, return a train of fully correlated pulses （Swerling Ⅰ model） or fully decorrelated pulses （Swerling Ⅱ model）. The detection of this type of fluctuating targets is therefore of great importance. This paper is devoted to the analysis of Cell-Averaging （CA） based detectors for the case where the radar receiver noncoherently integrates M square-law detected pulses and the signal fluctuation obeys 2 statistics with two degrees of freedom. These detectors include the Mean-Of （MO）, the Greatest-Of （GO） and the Smallest-Of（SO） schemes. In these processors, the estimation of the noise power levels from the leading and the trailing reference windows is based on the CA technique. Exact formulas for the detection probabilities are derived, in the absence as well as in the presence of spurious targets. The primary and the secondary interfering targets are assumed to be fluctuating in accordance with the χ^2 fluctuation model with two degrees of freedom （SWI ＆ SWII）. The numerical results show that the MO version has the best homogeneous performance, the SO scheme has the best multiple-target performance, while the GO procedure does not offer any merits, neither in the absence nor in the presence of outlying targets.

High-resolution forward-looking imaging algorithm for missile-borne detectors

Aiming at a novel missile-borne detector in the optional burst height proximity fuze, a self-adaptive high-resolution forward-looking imaging algorithm (SAHRFL-IA) is presented. The echo data are captured by the missile-borne detector in the target regions;thereby the azimuth angulation accuracy at the same distance dimension is improved dynamically. Thus, azimuth information of the targets in the detection area may be obtained accurately. The proposed imaging algorithm breaks through the conventional misconception of merely using azimuth discrimination curves under ideal conditions during monopulse angulation. The real-time echo data from the target region are used to perform error correction for this discrimination curve, and finally the accuracy of the azimuth angulation may reach the optimum at the same distance dimension. A series of experiments demonstrate the validity, reliability and high performance of the proposed imaging algorithm. Azimuth angulation accuracy may reach ten times that of the detection beam width. Meanwhile, the running time of this algorithm satisfies the requirements of missile-borne platforms.

月基极紫外相机光子计数成像探测器Ge膜的空间质子辐照稳定性

光子计数成像探测器是月基极紫外相机等空间成像载荷的核心成像器件。Ge膜作为探测器的电荷感应层,在空间质子辐照环境下的稳定性直接影响系统的成像质量。采用Monte-Carlo法仿真了500 nm Ge膜被10、50和100 keV高能质子辐照后,Ge膜内部的质子浓度分布与空位缺陷分布。仿真结果表明,50 keV质子在Ge膜内部产生的空位缺陷对膜层内部损伤最大。对100、230、350和500 nm厚度Ge膜分别进行50 keV质子辐照实验,经方阻仪测试显示,越厚的薄膜辐照后方阻变化越大,500 nm比100 nm Ge膜方阻增加量高约500 MΩ/。通过原子力显微镜(AFM)表征发现,500 nm Ge膜辐照后出现膜层凸起与分离,均方根表面粗糙度由1.63 nm增至约10 nm。研究结果表明,在满足方阻要求的前提下,通过对Ge膜厚度的合理设计,可以有效减小高能质子辐照带来的损伤。

基于检测器与定位器融合的自适应校正跟踪算法

针对传统目标跟踪算法在复杂动态场景中因目标发生遮挡、旋转等多种因素而导致的跟踪失败问题,提出了一种基于检测器与定位器融合的自适应校正跟踪算法。定位器通过提取目标的深度特征训练CNN(convolutional neural network)滤波器进行位置估计,CNN滤波器在原CF2(hierarchical convolutional features for visual tracking)算法的3层卷积特征的基础上加入了2层浅层特征,增强了对目标纹理信息的提取。检测器通过提取目标的HOG(histogram of oriented gradient)特征,结合上下文信息计算置信度评分,用当前帧的平均峰值能量和响应最大值分别与历史均值比较,综合判断是否因为遮挡等因素导致跟踪失败,如果跟踪失败,结合检测器进行目标的重定位,否则对目标进行尺度估计。当模型具有高置信度时,更新模型。实验结果表明:算法距离精度和重叠精度均取得不错的效果。

红外影像奇偶元条纹噪声自适应去除算法

由于工艺原因,TDI型红外探测器的光敏元均为交错分布,奇偶元响应输出均是基于不同的通道,这样导致某些TDI红外探测器图像上一些位置仍残留有奇偶元条纹噪声,该噪声不仅影响目视效果,也影响后续的定量应用。文章针对该奇偶元条纹噪声提出一种自适应的条纹噪声去除算法,此方法不仅可以自适应地检测出奇偶元条纹噪声并进行去除,也可以对闪元噪声进行有效检测及去除;最后,基于在轨图像进行了算法的验证,试验结果表明该算法可以有效的去除奇偶元条纹噪声。