Fastiterative methods for least squares estimations

Least squares estimations have been used extensively in many applications, e.g. system identification and signal prediction. When the stochastic process is stationary, the least squares estimators can be found by solving a Toeplitz or near-Toeplitz matrix system depending on the knowledge of the data statistics. In this paper, we employ the preconditioned conjugate gradient method with circulant preconditioners to solve such systems. Our proposed circulant preconditioners are derived from the spectral property of the given stationary process. In the case where the spectral density functions() of the process is known, we prove that ifs() is a positive continuous function, then the spectrum of the preconditioned system will be clustered around 1 and the method converges superlinearly. However, if the statistics of the process is unknown, then we prove that with probability 1, the spectrum of the preconditioned system is still clustered around 1 provided that large data samples are taken. For finite impulse response (FIR) system identification problems, our numerical results show that annth order least squares estimator can usually be obtained inO(n logn) operations whenO(n) data samples are used. Finally, we remark that our algorithm can be modified to suit the applications of recursive least squares computations with the proper use of sliding window method arising in signal processing applications.Research supported in part by HKRGC grant no. 221600070, ONR contract no. N00014-90-J-1695 and DOE grant no. DE-FG03-87ER25037.     

Parameter identification and position control for helical hydraulic rotary actuators based on particle swarm optimization

It is difficult to obtain an accurate mathematical model in electro-hydraulic servo control system, due to the nonlinear factors such as dead zone, saturation, flow coefficient, and friction. Hence, a parameter identification algorithm, combining recursive least squares (RLS) with modified nonlinear particle swarm optimization (NPSO) algorithm, is proposed. On this basis, another improved NPSO algorithm is also put forward, aiming at searching for the optimal proportional–integral (PI) controller gain of the nonlinear hydraulic system while giving comprehensive consideration to the system performance indexes. The system identification experiments and position tracking control are conducted, respectively. As indicated by the comparison with the least squares (LS), RLS, PSO, and RLS–LPSO results, the proposed method shows higher identification and control accuracy.     

一种应用DS证据理论的雷达目标与IFF点迹关联方法

针对目标数量多、目标构成复杂环境下雷达目标与敌我识别(Identification Friend or Foe, IFF)点迹关联不准确的问题,提出了一种基于DS(Dempster-Shafer)证据理论的关联方法。基于区间灰数模型完成雷达目标与IFF点迹的灰关联度计算,并据此生成DS证据理论中辨识框架的基本概率赋值;利用Dempster规则对证据进行组合,当证据之间存在冲突时采用改进Murphy方法对数据进行处理;最终通过概率转换方法完成关联判决,形成对目标敌我属性的判定。典型场景下的仿真结果表明,该方法能够实现雷达目标与IFF点迹的有效关联,通过多次询问及关联过程,可提升不同场景下的关联正确率。     

一种信号调制识别网络的轻量化设计

神经网络在信号调制识别领域得到了广泛关注和研究。针对现有调制识别算法为提高识别准确率,导致模型尺寸过大、计算时间过长的问题,提出了一种调制识别神经网络的轻量化设计方案。该方案由信号失真校正模块和分类模块两部分组成。其中,信号失真校正模块通过参数估计器提取相位偏移信息,再经参数转换器对相位偏移进行参数校正,保证信号识别精度;分类模块由一维卷积神经网络(One-Dimensional Convolutional Neural Network, 1D-CNN)、选通递归单元(Gated Recurrent Unit, GRU)和高斯衰减层构成,从时间和空间的角度有效提取信号特征,并减少冗余参数量以缩减模型大小。仿真结果表明,所提方案与同精度网络相比,平均识别准确率提升0.21%,计算时间缩减到1/3.4,模型尺寸缩减到1/7.77。     

基于HSV的乳腺癌细胞识别方法

乳腺癌是女性致死率最高的疾病之一,严重威胁女性的身体健康。目前,病理医生判断病症等级,仍通过观察病理切片中的增殖细胞个数,具有很大的主观性。本文提出了一种将RGB彩色空间和HSV彩色空间模型结合的方法,采用各分量的色度特征,通过量化分析建立色度特征方程,实现从病理图像中检测识别乳腺癌增殖细胞。经20组乳腺癌病理切片图像验证,准确性达到了94.2%,优于单色彩空间模型方法约4%,本文提出方法能够更精确识别乳腺癌细胞,为乳腺癌细胞识别提供了一种新方法。     

一种实时多尺度目标检测识别算法

无人机目标检测与识别任务中,目标随着飞行高度的改变尺寸发生显著变化。常规目标检测模型中,获取的小目标细节信息有限,检测精度较低;而适用于小目标的实时检测模型往往容易丢失大目标的背景信息,降低大目标的检测精度。针对以上多尺度目标检测识别任务难点,提出一种基于改进特征金字塔网络(Feature Pyramid Network, FPN)结构的实时多尺度目标检测识别模型。该模型通过增加特征金字塔层级覆盖更广的目标尺度,获取更为丰富的目标信息;同时,利用跨连接增加不同尺度特征融合的多样性,降低特征传导距离,保留更加完整的尺度特征来提高模型检测识别多尺度目标的性能。通过实验发现,相比于原始网络结构和相同特征层级的四层特征金字塔结构,加入改进特征金字塔结构的多尺度目标检测模型识别性能得到了提升。     

面向典型战场应用的目标智能融合技术

针对战场多源信息融合的典型应用需求,提出了基于人工智能的目标信息融合技术思路,并对其中的两项关键技术,即新型融合知识库构建技术和基于本体核的目标知识融合技术进行了分析,最后对多源信息融合系统在应用过程中存在的技术问题及其解决思路进行了探讨。     

TETRA通信与干扰信号智能识别技术

陆上集群无线电(Terrestrial Trunked Radio,TETRA)数字集群通信系统因其开放性易受到内外部电磁干扰。干扰信号的类型多种多样,针对不同的干扰样式,采取的抗干扰措施也各不相同,因此干扰信号的识别具有重大意义。基于此,提出了一种干扰信号智能识别技术。该技术首先对受到不同干扰后的TETRA音频数据进行特征提取,并筛选出具有分类能力的特征,其次使用决策树、支持向量机和随机森林3种分类模型对特征提取后的待测试信号进行智能分类识别。实验结果表明,使用的这3种模型能够有效判断TETRA系统中的信号是否受到干扰,以及受到何种样式的干扰,可为后续TETRA系统中的信号干扰识别提供参考。     

基于Focal-EIOU函数的被动式太赫兹图像违禁物品识别

针对被动式太赫兹安检系统因环境影响导致图像质量波动,从而影响识别算法,导致准确率大幅降低的问题,提出了基于Focal-EIOU损失函数的改进YOLOv4算法,并用被动式太赫兹人体安检图像对刀、枪违禁物品进行模型训练获得模型。建立不同环境、不同位置角度携带刀枪嫌疑物人员的太赫兹图像数据库,采用图像增广的方法构建丰富数据集;将YOLOv4的CIOU loss改进为Focal-EIOU loss,提高算法对太赫兹图像识别的鲁棒性,进而经过训练获得较优的模型。在本文的测试集中,使用改进后的算法训练的模型平均检测精确度(mAP)达到96.4%,检测速度在28 ms左右,交并比(IOU)平均值为0.95,在同等条件下高于常规算法,改善了检测识别的效果。实验结果表明,本文方法能够有效提高被动式太赫兹人体安检系统的嫌疑物识别准确率,有利于该项技术在人体安检领域的推广应用。     

RFID-based 3D human pose tracking: A subject generalization approach

Three-dimensional (3D) human pose tracking has recently attracted more and more attention in the computer vision field. Real-time pose tracking is highly useful in various domains such as video surveillance, somatosensory games, and human-computer interaction. However, vision-based pose tracking techniques usually raise privacy concerns, making human pose tracking without vision data usage an important problem. Thus, we propose using Radio Frequency Identification (RFID) as a pose tracking technique via a low-cost wearable sensing device. Although our prior work illustrated how deep learning could transfer RFID data into real-time human poses, generalization for different subjects remains challenging. This paper proposes a subject-adaptive technique to address this generalization problem. In the proposed system, termed Cycle-Pose, we leverage a cross-skeleton learning structure to improve the adaptability of the deep learning model to different human skeletons. Moreover, our novel cycle kinematic network is proposed for unpaired RFID and labeled pose data from different subjects. The Cycle-Pose system is implemented and evaluated by comparing its prototype with a traditional RFID pose tracking system. The experimental results demonstrate that Cycle-Pose can achieve lower estimation error and better subject generalization than the traditional system.