一种基于子孔径的GMTI方法

该文研究了一种基于子孔径分割的GMTI(Ground Moving Target Indication)方法。在该方法中将天线孔径沿方位向对称分割为两个子孔径。当天线波束在空间扫描时,两个子孔径先后扫过目标所在区域。由于两个子孔径之间有二分之一合成孔径时间的时间差,动目标在前后两个孔径中运动状态发生改变,而静止目标的运动状态不变。因此,可以通过比较目标在两个子孔径中运动状态的差异发现动目标,并根据目标轨迹判断其运动参数。为了避免方位压缩造成的动目标模糊或动目标能量损失,同时为保留目标运动轨迹,这里不进行方位压缩处理。与多通道GMTI方法相比较,此方法具有只需一副单通道天线,处理方法简单,运算量小,可与合成孔径雷达工作模式兼容等诸多优势。     

一种机载GMTI雷达点迹凝聚方法研究

机载GMTI雷达对地面运动目标探测时,由于目标在距离和方位向扩散严重,在进行目标凝聚时难以获取目标的精确位置,导致目标跟踪精度差、虚警率高,难以满足实际使用需求。针对这一难题,本文提出了一种针对机载GMTI雷达原始点迹的凝聚方法,首先基于构建的点扩散函数对目标进行信号幅度复原处理,再进行点迹质心凝聚,并对凝聚后的点迹进行二次检测,对检测后的凝聚点迹进行跟踪处理。通过对实际飞行数据进行分析处理,验证了本方法对提升目标跟踪精度、降低跟踪虚警率的有效性。     

一种基于拓扑约束的多核跟踪算法

针对视觉跟踪中常见的目标部分遮挡和尺度变化问题,提出了一种基于拓扑约束的多核跟踪算法.首先,提取满足空间分布的位于目标与背景所在边界的Harris角点作为多核跟踪器的中心,然后,采用拓扑约束对多个跟踪结果进行优化,选取跟踪性能好的核跟踪器,构造仿射变换模型,进而得到最终跟踪结果和目标尺度变化信息.实验结果表明,本文算法能对目标进行准确跟踪,并能有效地处理目标的部分遮挡和尺度变化问题.     

一种利用掩模法抑制杂波的GMTI方法

该文研究了一种利用掩模抑制杂波的GMTI(Ground Moving Target Indication)方法。在该方法中利用子孔径对消形成动目标掩模。由于静目标在方位向两个子孔径中的表现相同而动目标不同,因此在该掩模上静目标被消掉,动目标的痕迹得以保留。利用此掩模与距离压缩方位未聚焦的复图像相乘,以恢复动目标的幅度和相位。乘积的复图像采用比静目标更宽频带的参考函数进行方位聚焦,并采用自聚焦的方法进一步聚焦,以提高信噪比和动目标的发现概率。该方法适合运动速度较大的目标,和ATI(Along-Track Interferometry)方法相结合可以有效增大动目标的速度检测范围。     

一种高精度机载GMTI雷达数据滤波方法

针对机载GMTI雷达地面机动慢速多目标跟踪难题,提出了一种新颖的高精度机载GMTI雷达数据滤波跟踪方法。针对机载GMTI相控阵雷达的特点,引入一种新的机载GMTI雷达量测模型,采用Doppler伪量测形式减弱Doppler量测与目标状态之间的强非线性关系,并进行了量测误差转换;通过论述发现,采用运算速度较快的UKF滤波器进行非线性滤波估计,可以有效地降低由机体坐标系到地面站惯性坐标系的复杂变换引起的估计误差放大。实验证明,该方法可以有效地提高地面机动多目标的跟踪精度,满足实际雷达跟踪系统要求。     

一种基于道路约束的变结构多模型跟踪算法

有效使用道路信息能显著提高地面运动目标跟踪性能。针对复杂道路网运动目标跟踪,给出了一种道路约束下的滤波和建模方法。首先描述了道路网的数学模型和道路约束条件下的过程噪声修正方法,根据道路约束最大后验概率估计准则给出了一种路段切换时状态和协方差修正公式;接着提出一种根据目标当前所处路段自适应滤波模型生成的变结构多模型(VSMM)方法,有效减小道路运动目标的模型动态误差,提高跟踪性能。仿真结果证明了该算法的优越性能。     

干扰条件下基于UPF的组网雷达目标跟踪

为解决压制性干扰对组网雷达跟踪滤波的影响,引入系统量测合成方法来解决测量信息不全的问题,采用UPF算法来解决非线性非高斯背景下的目标跟踪问题。仿真结果表明系统量测合成与UPF结合的方法,可以充分利用组网雷达"残留"信息,能够有效对目标进行跟踪,在跟踪精度和时效性上均优于标准PF算法。     

一种改进的多通道干涉SAR／GMTI方案

该文分析了多通道干涉SAR／GMTI的工作原理，讨论了系统中不同误差源对主杂波抑制的影响，在此基础上，对多通道干涉SAR／GMTI方案中最关键的对消因子提出了改进，仿真结果表明；经改进的多通道干涉SAR／GMTI方案对各种误差的敏感程度显著下降，具有较好的鲁棒性，因而改进后的方案更适合于工程实施。     

一种基于UPF的干涉SAR相位展开方法

提出一种不敏粒子滤波的相位展开方法.该方法是不敏粒子滤波器与路径跟踪策略以及全方位的局部相位梯度估计相结合的结果,不受模型噪声统计特性和线性条件约束,同时完成噪声消除和相位展开;利用不敏卡尔曼滤波器来进行粒子更新,使重要密度函数能够融入最新观测信息和更加符合真实状态的后验概率分布,从而提高了相位展开精度与效率.仿真和实...     

一种基于卡尔曼滤波的运动目标跟踪方法

运动目标信息探测大量地体现在现实生活以及军事应用中,准确地跟踪并定位运动目标对于国防技术发展具有很大的现实意义和实际价值。以运动物体为目标,提出了采用自适应波门跟踪与卡尔曼滤波算法相结合的技术,有效地跟踪目标并将预测结果与其实际运动轨迹比较,取其误差均值、标准差值,用以证实卡尔曼滤波跟踪目标的有效性。     

5G核心网UPF硬件加速技术

5GC通过NFV技术在通用硬件上实现网元功能,具有资源灵活共享等优点,但是5G的uRLLC、eMBB等应用有超低时延、高带宽等要求,对核心网UPF的转发时延、带宽、抖动、丢包率等性能提出了更高要求。将部分业务处理卸载到适合大规模转发和并行计算的硬件加速卡,可提供更好的转发特性,总结并分析了UPF的转发流程,并对当前的硬件加速技术和加速方案进行介绍,提出了目前UPF硬件加速技术中存在的问题,最后对UPF硬件加速研究方向和思路进行展望。     

Mobility Tracking in Cellular Networks Using Particle Filtering

Mobility tracking based on data from wireless cellular networks is a key challenge that has been recently investigated both from a theoretical and practical point of view. This paper proposes Monte Carlo techniques for mobility tracking in wireless communication networks by means of received signal strength indications. These techniques allow for accurate estimation of mobile station's (MS) position and speed. The command process of the MS is represented by a first-order Markov model which can take values from a finite set of acceleration levels. The wide range of acceleration changes is covered by a set of preliminary determined acceleration values. A particle filter and a Rao-Blackwellised particle filter are proposed and their performance is evaluated both over synthetic and real data. A comparison with an extended Kalman filter (EKF) is performed with respect to accuracy and computational complexity. With a small number of particles the RBPF gives more accurate results than the PF and the EKF. A posterior Cramer Rao lower bound (PCRLB) is calculated and it is compared with the filters' root- mean-square error performance.     

一种基于神经网络跟踪控制的稳定性分析

本文针对一种标准形式的未知非线性系统的输出跟踪控制构造了一种基于多层前馈神经网络的的非线性学习控制系统，并利用ＢＰ学习算法训练好的神经网络构造了一个自适应输出跟踪控制结构。     

A Constraint-Elimination Technique for Linearly Constrained Array Processing

A new technique is presented for the problem of linearly constrained multi-channel-array processing. The purpose of the array processing is to reject undesired noise, in the minimum-mean-squared-error sense, while responding to any signal coming from a particular direction with a preset, constrained frequency response. In the constraint-elimination technique proposed in this paper, the original constrained-array-processing problem is transformed to the simple unconstrained-Wiener-filtering problem. Transformation of the problem is achieved by introducing a ?compensating? channel to the multichannel processor. Input to the compensating channel is the channel-averaged input. The constraint is then eliminated by expressing the filter weights of the compensating channel in terms of those of the original processor. An adaptive algorithm is derived by applying the stochastic approximation to the simplified Wiener-filtering problem. Simulation experiments verify that the constrained-array-processing problem is properly transformed to the unconstrained one by the proposed technique. Additional experiments show that the adaptive algorithm generally reduces output power while maintaining the constrained frequency response to the desired signal.     

Tracking Traffic Peaks in Mobile Networks Using Statistics of Performance Metrics

In recent years, there has been an increasing awareness to tracking traffic peaks reflecting the presence of mass events or permanent traffic hotspots. This trend is driven by dominant themes for wireless evolution towards 5G networks such as the problematic of hotspot offloading solutions, the emergence of heterogeneous networks with small cells’ deployment and the development of green networks’ concept. Actually, tracking traffic peaks with a high accuracy is of great interest to know how the congested zones can be offloaded, where small cells should be deployed and how they could be managed for sleep mode concept or even controlled according to traffic mobility if they are moving. In this paper, we propose a method for tracking peaks of traffic using performance metrics extracted from the operation and maintenance database of the network. These metrics are the timing advance, the angle of arrival, the neighboring cell level, the cell load and two mean throughputs: arithmetic (AMT) and harmonic (HMT). The combined use of these performance metrics, projected over a coverage map, yields a promising traffic localization precision even with considering imperfections of coverage prediction and mobile equipment capabilities in handling measurements. The proposed solution can be easily implemented in the network at an appreciable low cost.     

Modeling Mobility Tracking Procedures in PCS Systems Using Stochastic Activity Networks

In this paper the capability of stochastic activity networks (SANs) has been used as a novel approach in the analysis of mobility tracking procedures. To evaluate complex systems such as cellular systems, powerful tools for modeling and analysis are required. SANs have proved to be very flexible and powerful tools in the study of location update (LU) and paging (PG) schemes. SANs can capture, in a simple manner and from the mobility models under study, much more detail than Markov models. Therefore, the analysis of mobility tracking procedures with SANs provides more accuracy than the use of Markov models. This fact is illustrated in the paper by considering the LU and PG schemes proposed in Ref. [12]. Furthermore, our results show a much better performance of these schemes than that predicted by Ref. [12].     

Tracking in Wireless Sensor Networks Using Particle Filtering: Physical Layer Considerations

In this paper, a new framework for target tracking in a wireless sensor network using particle filters is proposed. Under this framework, the imperfect nature of the wireless communication channels between sensors and the fusion center along with some physical layer design parameters of the network are incorporated in the tracking algorithm based on particle filters. We call this approach ldquochannel-aware particle filtering.rdquo Channel-aware particle filtering schemes are derived for different wireless channel models and receiver architectures. Furthermore, we derive the posterior Cramer-Rao lower bounds (PCRLBs) for our proposed channel-aware particle filters. Simulation results are presented to demonstrate that the tracking performance of the channel-aware particle filters can reach their theoretical performance bounds even with relatively small number of sensors and they have superior performance compared to channel-unaware particle filters.     

基于受限信任关系和概率分解矩阵的推荐

现有的推荐算法很难对没有任何记录的冷启动用户或者历史记录稀疏的用户给出准确的推荐,即用户的冷启动问题.本文提出一种基于受限信任关系和概率分解矩阵的推荐方法,由不信任关系约束信任关系的传播,得到准确且覆盖全面的用户信任关系矩阵,并通过对用户信任关系矩阵和用户商品矩阵的概率分解联合用户信任关系和用户商品矩阵信息,为用户给出推荐.实验表明该方法对冷启动用户和历史记录稀疏的用户的推荐效果有较大幅度的提升,有效地解决了用户的冷启动问题.     

Design of Constrained Causal Stable IIR Filters Using a New Second- Order-Cone-Programming-Based Model-Reduction Technique

This brief proposes a new method for designing infinite-impulse response (IIR) filter with peak error and prescribed flatness constraints. It is based on the model reduction of a finite-impulse response function that satisfies the specification by extending a method previously proposed by Brandenstein. The proposed model-reduction method retains the denominator of the conventional techniques and formulates the optimal design of the numerator as a second-order cone programming problem. Therefore, linear and convex quadratic inequalities such as peak error constraints and prescribed number of zeros at the stopband for IIR filters can be imposed and solved optimally. Moreover, a method is proposed to express the denominator of the model-reduced IIR filter as a polynomial in integer power of z, which efficiently facilitates its polyphase implementation in multirate applications. Design examples show that the proposed method gives better performance, and more flexibility in incorporating a wide variety of constraints than conventional methods