Trajectory Tracking for MmWave Communication Systems via Cooperative Passive Sensing

A cooperative passive sensing framework for millimeter wave(mmWave)communication systems is proposed and demonstrated in a scenario with one mobile signal blocker.Specifically,in the uplink communication with at least two transmitters,a cooperative detection method is proposed for the receiver to track the blocker’s trajectory,localize the transmitters and detect the potential link blockage jointly.To facilitate detection,the receiver collects the signal of each transmitter along a line-of-sight(LoS)path and a non-line-of-sight(NLoS)path separately via two narrow-beam phased arrays.The NLoS path involves scattering at the mobile blocker,allowing its identification through the Doppler frequency.By comparing the received signals of both paths,the Doppler frequency and angle-of-arrival(AoA)of the NLoS path can be estimated.To resolve the blocker’s trajectory and the transmitters’locations,the receiver should continuously track the mobile blocker to accumulate sufficient numbers of the Doppler frequency and AoA versus time observations.Finally,a gradient-descent-based algorithm is proposed for joint detection.With the reconstructed trajectory,the potential link blockage can be predicted.It is demonstrated that the system can achieve decimeterlevel localization and trajectory estimation,and predict the blockage time with an error of less than 0.1 s.

Passive target tracking with intermittent measurement based on random finite set

In the tracking problem for the maritime radiation source by a passive sensor,there are three main difficulties,i.e.,the poor observability of the radiation source,the detection uncertainty(false and missed detections)and the uncertainty of the target appearing/disappearing in the field of view.These difficulties can make the establishment or maintenance of the radiation source target track invalid.By incorporating the elevation information of the passive sensor into the automatic bearings-only tracking(BOT)and consolidating these uncertainties under the framework of random finite set(RFS),a novel approach for tracking maritime radiation source target with intermittent measurement was proposed.Under the RFS framework,the target state was represented as a set that can take on either an empty set or a singleton; meanwhile,the measurement uncertainty was modeled as a Bernoulli random finite set.Moreover,the elevation information of the sensor platform was introduced to ensure observability of passive measurements and obtain the unique target localization.Simulation experiments verify the validity of the proposed approach for tracking maritime radiation source and demonstrate the superiority of the proposed approach in comparison with the traditional integrated probabilistic data association(IPDA)method.The tracking performance under different conditions,particularly involving different existence probabilities and different appearance durations of the target,indicates that the method to solve our problem is robust and effective.

POSTERIOR CRAMR-RAO BOUNDS ANALYSIS FOR PASSIVE TARGET TRACKING

For the problem of deterministic parameter estimate, the theoretical lower bound of esti- mate error is the Cramér-Rao bound; while for random parameter, the lower bound of estimate error is generally termed by Posterior Cramér-Rao Bound (PCRB). Under the background of passive tracking where the target's state can be seen as a time-varying random parameter, PCRB of the state estimate error is analyzed in this paper, and the relation between PCRB and varied condition is also fully in- vestigated using different simulation examples. The presented analytical method provides a theoretical base for performance assessment of all kinds of suboptimal estimate algorithms used in practice.

Adaptive Multisensor Tracking Fusion Algorithm for Air－borne Distributed Passive Sensor Network

Single passive sensor tracking algorithms have four disadvantages: bad stability, longdynamic time, big bias and sensitive to initial conditions. So the corresponding fusion algorithm results in bad performance. A new error analysis method for two passive sensor tracking system is presented and the error equations are deduced in detail. Based on the equations, we carry out theoretical computation and Monte Carlo computer simulation. The results show the correctness of our error computation equations. With the error equations, we present multiple 'two station'fusion algorithm using adaptive pseudo measurement equations. This greatly enhances the tracking performance and makes the algorithm convergent very fast and not sensitive to initial conditions.Simulation results prove the correctness of our new algorithm.

Passive target tracking using marginalized particle filter

A marginalized particle filtering （MPF） approach is proposed for target tracking under the background of passive measurement. Essentially, the MPF is a combination of particle filtering technique and Kalman filter. By making full use of marginalization, the distributions of the tractable linear part of the total state variables are updated analytically using Kalman filter, and only the lower-dimensional nonlinear state variable needs to be dealt with using particle filter. Simulation studies are performed on an illustrative example, and the results show that the MPF method leads to a significant reduction of the tracking errors when compared with the direct particle implementation. Real data test results also validate the effectiveness of the presented method.

Passive Target Tracking Based on Current Statistical Model

Bearing-only passive tracking is regarded as a nonlinear hard tracking problem. There are still no completely good solutions to this problem until now. Based on current statistical model, the novel solution to this problem utilizing particle filter (PF) and the unscented Kalman filter (UKF) is proposed. The new solution adopts data fusion from two observers to increase the observability of passive tracking. It applies the residual resampling step to reduce the degeneracy of PF and it introduces the Markov Chain Monte Carlo methods (MCMC) to reduce the effect of the “sample impoverish”. Based on current statistical model, the EKF, the UKF and particle filter with various proposal distributions are compared in the passive tracking experiments with two observers. The simulation results demonstrate the good performance of the proposed new filtering methods with the novel techniques.

Reliability of Passive Physical Activity Assessment for Inpatients Using Marker⁃Free Motion Tracking System

Evidence⁃based practices of public health will benefit from quantification of passive physical activity assessment.This study aims to investigate the reliability of marker⁃free system(MFS)such as Microsoft Kinect in measuring upper extremity motion from different angles.Ten healthy participants performed elbow and shoulder extension/flexion along frontal and median anatomical planes for ten pace⁃controlled repetitions,during which the spatiotemporal positions of upper extremity joints were concurrently recorded by two sensors from 0°and 45°viewing angles.Reliability between the two sensors were evaluated using Pearson correlation coefficient,intra⁃class correlation coefficients,and 95%limits of agreement and coefficient of variation.Worse reliability was observed when possibility of occlusion was higher.However,better reliability was found when longer observation interval(10 s)was used as elementary measuring unit than shorter observation interval(2 s).The overall angular reliability of activity as displacement or changes in angle was not satisfactory.The results are expected to inform the industry for the extension of MFS to clinic applications.

Passive Target Tracking in Non-cooperative Radar System Based on Particle Filtering

We propose a target tracking method based on particle filtering(PF) to solve the nonlinear non-Gaussian target-tracking problem in the bistatic radar systems using external radiation sources. Traditional nonlinear state estimation method is extended Kalman filtering (EKF), which is to do the first level Taylor series extension. It will cause an inaccuracy or even a scatter estimation result on condition that there is either a highly nonlinear target or a large noise square-error. Besides, Kalman filtering is the optimal resolution under a Gaussian noise assumption, and is not suitable to the non-Gaussian condition. PF is a sort of statistic filtering based on Monte Carlo simulation that is using some random samples (particles) to simulate the posterior probability density of system random variables. This method can be used in any nonlinear random system. It can be concluded through simulation that PF can achieve higher accuracy than the traditional EKF.

ALGORITHM FOR ACOUSTIC PASSIVE LOCALIZATION WITH DUAL ARRAYS

Aiming at the problem of 3D target localization by time delay estimation, this paper proposes a new acoustic passive localization method, which can provide high precision localization estimation. The first step of the two-stage algorithm is to measure the azimuth angle and pitch angle at each single array, which can obtain high precision angle estimation but low precision range estimation. And in the second step, the location of acoustic source is calculated from the angles measured above and geometry position of the two arrays. Then the accuracy of localization estimation is discussed in theory, and the influence factors and localization error are analyzed by simulation. The simulation results validate the performance of the proposed algorithm, and show the precision of localization estimation with dual arrays is superior to single array.

Posterior Cramer-Rao lower bounds for bearing-only tracking

In the state estimation of passive tracking systems, the traditional approximate expression for the Cramero-Rao lower bound （CRLB） does not take two factors into consideration, that is, measurement origin uncertainty aad state noise. Such treatment is only valid in ideal situation but it is not feasible in actual situation. In this article, considering the two factors, the posterior Cramer-Rao lower bound （PCRLB） recursion expression for the error of bearing-only tracking is derived. Then, further analysis is carried out on the PCRLB. According to the final result, there are four main parameters that play a role in the performance of the PCRLB, that is, measurement noise, detection probability, state noise and clutter density, amongst which the first two have greater impact on the performance of the PCRLB than the others.

New insights from low-temperature thermochronology into the tectonic and geomorphologic evolution of the south-eastern Brazilian highlands and passive margin

The South Atlantic passive margin along the south-eastern Brazilian highlands exhibits a complex landscape,including a northern inselberg area and a southern elevated plateau,separated by the Doce River valley.This landscape is set on the Proterozoic to early Paleozoic rocks of the region that once was the hot core of the Aracuai orogen,in Ediacaran to Ordovician times.Due to the break-up of Gondwana and consequently the opening of the South Atlantic during the Early Cretaceous,those rocks of the Araquai orogen became the basement of a portion of the South Atlantic passive margin and related southeastern Brazilian highlands.Our goal is to provide a new set of constraints on the thermo-tectonic history of this portion of the south-eastern Brazilian margin and related surface processes,and to provide a hypothesis on the geodynamic context since break-up.To this end,we combine the apatite fission track(AFT)and apatite(U-Th)/He(AHe)methods as input for inverse thermal history modelling.All our AFT and AHe central ages are Late Cretaceous to early Paleogene.The AFT ages vary between 62 Ma and90 Ma,with mean track lengths between 12.2μm and 13.6μm.AHe ages are found to be equivalent to AFT ages within uncertainty,albeit with the former exhibiting a lesser degree of confidence.We relate this Late Cretaceous-Paleocene basement cooling to uplift with accelerated denudation at this time.Spatial variation of the denudation time can be linked to differential reactivation of the Precambrian structural network and differential erosion due to a complex interplay with the drainage system.We argue that posterior large-scale sedimentation in the offshore basins may be a result of flexural isostasy combined with an expansion of the drainage network.We put forward the combined compression of the Mid-Atlantic ridge and the Peruvian phase of the Andean orogeny,potentially augmented through the thermal weakening of the lower crust by the Trindade thermal anomaly,as a probable cause for the uplift.

一种融合时差频差和测向的运动目标跟踪方法

传统的星载无源定位系统对空中辐射源定位求解通常采用假设高程的方法,高程假设误差将对定位跟踪精度造成较大影响。为实现未知高程运动辐射源的高精度定位跟踪,针对异轨三星构型的无源定位系统,提出了一种基于时差、频差和二维测向融合的迭代扩展卡尔曼滤波(Iterative Extended Kalman Filter,IEKF)跟踪方法。在WGS-84坐标系下建立了状态方程和观测方程,并采用IEKF方法对目标状态进行估计。仿真结果表明,该方法可对未知高程的运动目标进行高精度状态估计,典型仿真场景下的目标高程估计精度达到百米量级,相对于已有方法收敛时间更短,并且在卫星覆盖范围内具有更大的高精度定位跟踪区域。

基于多初级假设的FM广播外辐射源雷达网航迹起始算法

基于调频(FM)广播信号的外辐射源雷达有着检测概率低、虚警率高、量测精度差的特点,这给组网目标跟踪带来了极大挑战。一方面,较高的虚警率使计算量增加,组网算法的实时性受到考验;另一方面,检测概率低、方位角精度差造成冗余信息缺乏,量测关联与航迹起始变得困难。为解决这些问题,该文提出初级假设点和初级假设航迹的概念,以及基于此概念的FM广播外辐射源雷达网航迹起始算法。首先构造可能的低维关联假设,并解算出与其对应的初级假设点;随后关联不同时刻的初级假设点,形成多条可能的初级假设航迹;最后联合多场雷达网数据进行假设航迹判决,真实目标对应的初级假设航迹会得到确认,错误关联导致的虚假初级假设航迹会被剔除。相比于已有算法,所提算法有着更低的计算量,更快的航迹起始速度,仿真与实测结果均验证了所提算法的有效性。

无源声呐水下多目标融合跟踪方法

针对海洋环境噪声导致弱目标在不同子频带检测结果差异较大,致使以全频带探测结果为输入的跟踪算法出现性能退化的问题,提出一种子带融合跟踪方法。该方法利用改进的高斯混合概率假设密度滤波器对各频率子带输出的方位估计结果进行跟踪,并采用广义协方差交集准则对子带跟踪结果进行融合,以获得综合各子带信息的跟踪结果。仿真结果表明,所提方法可以提高弱目标在各子带信噪比不均衡情况下的跟踪能力,且运算时间与对比方法较为接近。海试数据处理结果进一步验证了所提方法的有效性。

联合多传感器的水下多目标无源声学定位

在多传感器无源声学定位问题中,不同传感器接收的来自同一目标的信号均对应于此目标位置,依据这一物理基础,提出了一种基于粒子滤波的无源声学定位方法,以有效融合多传感器数据,进而提高定位性能。该方法将粒子滤波中的似然函数定义为粒子状态所对应不同传感器信号之间互相关输出的乘积。该似然函数的设计确保所提方法可以充分获取多传感器的处理增益。此外,所提方法摆脱了传统定位范式,因此可以规避传统定位范式必须面对的测量−跟踪关联问题。湖上试验表明,在强多途干扰的条件下,传统定位方法的平均定位误差为7.2 m,而所提方法的平均定位误差为1.2 m,具有更好的性能。

基于改进ATPM-IMM算法的外辐射源雷达机动目标跟踪

针对外辐射源雷达进行机动目标跟踪时,现有的自适应交互式多模型(AIMM)算法难以达到高精确度跟踪的问题,提出一种基于改进的自适应转移概率交互式多模型(ATPM-IMM)的机动目标跟踪算法。该算法在ATPM-IMM算法的基础上增加了自适应控制窗,对转移概率矩阵进行再次修正,从而可根据目标的机动情况自适应切换机动模型,提高真实模型的匹配概率。仿真和实测数据结果表明,所提算法可有效提高外辐射源雷达进行机动目标跟踪的精确度。

基于不确定区域的水下纯方位目标跟踪方案

围绕水下被动目标跟踪问题,目前的研究通常以最优估计点迹表征被测目标跟踪状态,而点估计无法表达示向性的位置误差信息,导致无法较好地为实际战场提供决策支持。针对上述问题,该文提出一种基于不确定区域(AOU)的水下纯方位目标跟踪方案。首先,提出一种基于变权解析的定位算法以获得精确的目标位置信息,将目标位置作为AOU构建算法的先验知识。然后,分别通过有无滤波不确定区域构造算法,输出目标位置不确定区域。通过对不同仿真态势下AOU的评估指标进行统计分析,结果表明利用该目标跟踪方案均能对目标实现可靠精确的位置估计,说明该文提出的基于不确定区域的目标跟踪方案能够有效完成目标跟踪任务。该方案优势在于,目标估计结果包含示向性位置误差和区间估计的置信度,为后续决策提供清晰的容错与判断区域,具有更好的参考价值及实用价值。

天基分布式无源探测的空间多目标跟踪方法

针对巨型星座群等空间非合作目标实时跟踪难题,提出一种天基分布式协同无源探测的多目标跟踪方法。首先,建立了地球非球形J2项摄动和大气阻力摄动条件下的轨道动力学模型,并建立了星载的单位视线矢量测量模型。然后,建立了基于势均衡概率假设密度滤波的多目标跟踪算法,并采用高斯混合方法求得多维积分的近似封闭解,降低算法的计算复杂度,以解决星载实现问题。接着,设计了多平台多目标跟踪交互的一致性信息融合方案,引入标签进行目标区分,减少不同平台之间信息传递与融合带来的计算匹配问题,并使用一致性信息滤波进行信息融合。最后,以某星座局部区域的15颗轨道相近的星座卫星作为跟踪目标,对所提方法进行仿真实验验证。仿真结果表明所提方法有效,跟踪性能比传统方法提升了约60%,在协同构型不奇异情况下跟踪的位置误差在5 km以内。

机械臂力/位跟踪的无源自抗扰控制器设计

为了提高全方位移动机械臂的力/位跟踪控制精度,设计了无源自抗扰控制器。介绍了全方位移动机械臂工作原理,基于拉格朗日方程建立了移动机械臂系统的动力学方程。根据严格无源系统定义,证明了全方位移动机械臂系统的无源特性。以自抗扰控制为基础,通过定义新变量对扩展状态观测器进行改进;针对机械臂系统的无源特性,设计了无源自抗扰控制器。在扰动工况下进行仿真验证,改进扩展状态观测器的扰动估计误差远小于传统扩展状态观测器,且估计速度快于传统扩展状态观测器;无源自抗扰控制器的跟踪累计绝对误差远小于传统自抗扰控制,说明无源自抗扰控制的控制精度高于传统自抗扰控制。仿真结果证明了无源自抗扰控制在全方位移动机械臂控制中的高精度和优越性。

High-accuracy target tracking for multistatic passive radar based on a deep feedforward neural network

In radar systems,target tracking errors are mainly from motion models and nonlinear measurements.When we evaluate a tracking algorithm,its tracking accuracy is the main criterion.To improve the tracking accuracy,in this paper we formulate the tracking problem into a regression model from measurements to target states.A tracking algorithm based on a modified deep feedforward neural network(MDFNN)is then proposed.In MDFNN,a filter layer is introduced to describe the temporal sequence relationship of the input measurement sequence,and the optimal measurement sequence size is analyzed.Simulations and field experimental data of the passive radar show that the accuracy of the proposed algorithm is better than those of extended Kalman filter(EKF),unscented Kalman filter(UKF),and recurrent neural network(RNN)based tracking methods under the considered scenarios.