CRAMER-RAO BOUNDS OF THE FREQUENCY ESTIMATION IN THE TIME SELECTIVE RADIO CHANNELS WITH DOPPLER SPREAD

The mobile channel is slow fading and time selective, thus the multiplicative and additive noise of the channel will smear the spectral line, or arouse Doppler spread. This spread will make the parameters estimation accuracy degrade. The goal of this paper is to analytically assess this degradation when Carrier Frequency Offset (CFO) and Doppler shift exist jointly. Then the finite-sample Cramer-Rao Lower Bound (CRLB) is derived and close-form asymptotical expression is given for large-sample CRLB. These expressions give insights into the performance room for frequency estimation. Also the variance of Doppler shift estimator is simulated to illustrate the theoretical results.

非理想检测下多雷达网络节点选择与辐射资源联合优化分配算法

该文针对分布式相控阵多雷达网络的多目标跟踪场景,研究非理想检测条件下的节点选择与辐射资源联合优化分配算法。首先,根据分布式相控阵多雷达网络构成、目标运动模型、雷达量测模型以及雷达节点检测情况,推导非理想检测下以雷达节点选择、辐射功率和信号带宽为变量的贝叶斯克拉默-拉奥下界(BCRLB)闭式解析表达式,并以此作为多目标跟踪精度衡量指标。在此基础上,以最小化系统各雷达节点对所有目标的总辐射功率为优化目标,以满足目标跟踪精度门限以及给定的系统射频辐射资源限制为约束条件,建立非理想检测条件下多雷达网络节点选择与辐射资源联合优化分配模型,对各时刻雷达节点选择、辐射功率和信号带宽等参数进行联合优化设计,以提升多雷达网络的射频隐身性能。最后,针对上述非线性、非凸优化问题,采用基于障碍函数法和循环最小化算法的4步分解算法进行求解。仿真结果表明,与现有算法相比,所提算法能在满足给定多目标跟踪精度的条件下有效降低分布式相控阵多雷达网络的总辐射功率,至少降低了约32.3%,从而提升其射频隐身性能。

基于距离向BCRLB的加权融合目标跟踪

推导了目标跟踪距离向贝叶斯克拉美罗下界(BCRLB)的表达式,将距离向BCRLB作为目标跟踪性能的量化指标。针对双机目标跟踪场景,采用交互多模型卡尔曼滤波,提出了基于距离向贝叶斯克拉美罗下界的加权融合算法。仿真实验表明,该算法的目标跟踪航迹与真实航迹贴合较好,跟踪过程中均方根误差能快速收敛到一个稳定的值,相比基于信噪比加权融合的目标跟踪算法,该算法的目标跟踪精度更高。

基于稀疏贝叶斯推断的密集城区内无人机目标直接定位算法

在当今社会,无人机“黑飞”现象日益频繁,给社会治理和公共安全带来了新的挑战。为了有效打击这一现象,迫切需要采取高精度的定位算法,以确保对无人机目标位置的准确获取。在一些密集城区内,定位设备的阵列天线接收到的信号是无人机经周边大量建筑物所构成的散射体散射后形成的多径分量的叠加,此时不能简单认为由点信源产生的,而是需要将目标建立为分布式信源模型。在这种情况下,如果仍采取传统的直接定位算法,在估计分布式信源位置时会出现性能急剧恶化的问题。针对上述问题,本文提出一种利用稀疏贝叶斯推断对相干分布式信源目标进行直接定位的算法。本算法首先建立相干分布式信源场景下多阵列联合的目标定位模型;对其构建稀疏概率框架,在该框架下对稀疏信号和噪声施加先验信息;之后利用贝叶斯推断方法可以更新迭代出超参数的估计值,进而得到每个网格点上的功率谱值;最后通过多维搜索来获取最大谱峰值处位置,即为信源位置。本文还详细推导了在数据域下相干分布式信源直接定位的克拉美罗下界,为算法的估计性能提供了基准。数值仿真结果表明在相干分布式信源模型下所提算法相比子空间类算法有着更高的定位精度和鲁棒性,在较多阵元情况下定位性能能够逼近最大似然估计算法。

Bayesian Multidimensional Scaling for Location Awareness in Hybrid-Internet of Underwater Things

Localization of sensor nodes in the internet of underwater things(IoUT)is of considerable significance due to its various applications,such as navigation,data tagging,and detection of underwater objects.Therefore,in this paper,we propose a hybrid Bayesian multidimensional scaling(BMDS)based localization technique that can work on a fully hybrid IoUT network where the nodes can communicate using either optical,magnetic induction,and acoustic technologies.These communication technologies are already used for communication in the underwater environment;however,lacking localization solutions.Optical and magnetic induction communication achieves higher data rates for short communication.On the contrary,acoustic waves provide a low data rate for long-range underwater communication.The proposed method collectively uses optical,magnetic induction,and acoustic communication-based ranging to estimate the underwater sensor nodes’final locations.Moreover,we also analyze the proposed scheme by deriving the hybrid Cramer-Rao lower bound(H-CRLB).Simulation results provide a complete comparative analysis of the proposed method with the literature.

ON MULTIPATH SIGNAL RESOLUTION AND ITS PERFORMANCE BOUNDS

The muitipath signal resolution is reviewed in this paper.The problemsexisted and to be studied are pointed out.Theoretical analysis of the performance ofthe resolution for deterministic signal in the cases where the signal known or unknownis made.Their corresponding Cramer-Rao lower bounds(CRLB)are obtained.

目标动态威胁度驱动的分布式组网相控阵雷达资源优化分配算法

针对分布式组网相控阵雷达多目标跟踪(MTT)场景,该文提出一种目标动态威胁度驱动的波束分配与驻留时间联合优化算法。首先,在采用分布式组网架构的基础上,推导包含波束和驻留时间分配的贝叶斯克拉美罗界(BCRLB)。其次,基于目标实时运动状态构建综合威胁度评估尺度,按照威胁度为不同目标设计基于跟踪精度参考门限和贡献度的效用函数,以此衡量资源在多目标间的优先分配关系。随后,将该效用函数结合组网相控阵雷达系统资源,建立了目标动态威胁度驱动的波束分配与驻留时间联合优化模型。最后,采用一种基于奖励的迭代下降搜索算法进行求解。仿真结果表明,相较于平均资源分配方法,所提算法具备对若干差异性目标的跟踪精度需求感知能力,能够在基于多目标威胁度评估的基础上,有针对性地分配跟踪资源,从而有效提高组网相控阵雷达面对不同威胁度目标时的综合跟踪精度。

Asymptotic Performance Limits of Vehicular Location and Velocity Detection Towards 6G mmWave Integrated Communication and Sensing

In this paper,joint location and velocity estimation(JLVE)of vehicular terminals for 6G integrated communication and sensing(ICAS)is studied.We aim to provide a unified performance analysis framework for ICAS-based JLVE,which is challenging due to random fading,multipath interference,and complexly coupled system models,and thus the impact of channel fading and multipath interference on JLVE performance is not fully understood.To address this challenge,we exploit structured information models of the JLVE problem to render tractable performance quantification.Firstly,an individual closedform Cramer-Rao lower bound for vehicular localization,velocity detection and channel estimation,respectively,is established for gaining insights into performance limits of ICAS-based JLVE.Secondly,the impact of system resource factors and fading environments,e.g.,system bandwidth,the number of subcarriers,carrier frequency,antenna array size,transmission distance,spatial channel correlation,channel covariance,the number of interference paths and noise power,on the JLVE performance is theoretically analyzed.The associated closed-form JLVE performance analysis can not only provide theoretical foundations for ICAS receiver design but also provide a perfor mance benchmark for various JLVE methods。

Optimal maneuvering strategy of spacecraft evasion based on angles-only measurement and observability analysis

Spacecraft orbit evasion is an effective method to ensure space safety. In the spacecraft’s orbital plane, the space non-cooperate target with autonomous approaching to the spacecraft may have a dangerous rendezvous. To deal with this problem, an optimal maneuvering strategy based on the relative navigation observability degree is proposed with angles-only measurements. A maneuver evasion relative navigation model in the spacecraft’s orbital plane is constructed and the observability measurement criteria with process noise and measurement noise are defined based on the posterior Cramer-Rao lower bound. Further, the optimal maneuver evasion strategy in spacecraft’s orbital plane based on the observability is proposed. The strategy provides a new idea for spacecraft to evade safety threats autonomously. Compared with the spacecraft evasion problem based on the absolute navigation, more accurate evasion results can be obtained. The simulation indicates that this optimal strategy can weaken the system’s observability and reduce the state estimation accuracy of the non-cooperative target, making it impossible for the non-cooperative target to accurately approach the spacecraft.

Parameter estimation of LFM signals based on time reversal

In this paper,parameter estimation of linear frequency modulation(LFM)signals containing additive white Gaussian noise is studied.Because the center frequency estimation of an LFM signal is affected by the error propagation effect,resulting in a higher signal to noise ratio(SNR)threshold,a parameter estimation method for LFM signals based on time reversal is proposed.The proposed method avoids SNR loss in the process of estimating the frequency,thus reducing the SNR threshold.The simulation results show that the threshold is reduced by 5 dB compared with the discrete polynomial transform(DPT)method,and the root-mean-square error(RMSE)of the proposed estimator is close to the Cramer-Rao lower bound(CRLB).

Performance Evaluation of Three-Dimensional UWB Real-Time Locating Auto-Positioning System for Fire Rescue

Fire rescue challenges and solutions have evolved from straightfor-ward plane rescue to encompass 3D space due to the rise of high-rise city buildings.Hence,this study facilitates a system with quick and simplified on-site launching and generates real-time location data,enabling fire rescuers to arrive at the intended spot faster and correctly for effective and precise rescue.Auto-positioning with step-by-step instructions is proposed when launching the locating system,while no extra measuring instrument like Total Station(TS)is needed.Real-time location tracking is provided via a 3D space real-time locating system(RTLS)constructed using Ultra-wide Bandwidth technology(UWB),which requires electromagnetic waves to pass through concrete walls.A hybrid weighted least squares with a time difference of arrival(WLS/TDOA)positioning method is proposed to address real path-tracking issues in 3D space and to meet RTLS requirements for quick computing in real-world applications.The 3D WLS/TDOA algorithm is theoretically constructed with the Cramer-Rao lower bound(CRLB).The computing complexity is reduced to the lower bound for embedded hardware to directly compute the time differential of the arriving signals using the time-to-digital converter(TDC).The results of the experiments show that the errors are controlled when the positioning algorithm is applied in various complicated situations to fulfill the requirements of engineering applications.The statistical analysis of the data reveals that the proposed UWB RTLS auto-positioning system can track target tags with an accuracy of 0.20 m.

频谱共存下机载组网雷达辐射资源与航迹规划联合优化算法

为了提升频谱共存环境下机载组网雷达多目标跟踪性能,提出了一种雷达辐射资源与航迹规划联合优化算法。首先,推导了包含雷达辐射资源以及载机飞行参数的贝叶斯克拉美-罗下界表达式,以此作为多目标跟踪精度的衡量指标;在此基础上,以最小化机载组网雷达多目标跟踪误差为优化目标,以雷达辐射资源总量、载机机动性能、通信基站可容忍干扰能量和跟踪各目标节点数为约束条件,建立频谱共存下机载组网雷达辐射资源与航迹规划联合优化模型;然后,将上述优化模型分解为多个子问题,并结合循环最小化算法、半正定规划算法和粒子群算法进行分步求解。最终仿真结果表明,所提算法能够在保证通信基站正常工作的条件下,充分利用各平台运动优势,与现有算法相比,能够有效提升机载组网雷达的多目标跟踪性能。

贝叶斯模型下的OMP重构算法及应用

针对稀疏度先验信息缺失的条件下,正交匹配追踪(orthogonal matching pursuit,OMP)算法设置冗余稀疏度时,造成信号过重构、抗噪性能变差等问题,基于贝叶斯检验模型,提出了贝叶斯正交匹配追踪(Bayesian orthogonal matching pursuit,BOMP)算法。并推导了该算法估计信号的克拉美罗下界,最后将算法应用于逆合成孔径雷达(inverse synthetic aperture radar,ISAR)成像。理论分析和实验结果表明,由于该算法能够更加真实地估计信号支撑集,因而具有更好的重构精度、抗噪性能,同时降低了计算复杂度。

Bayes估计的中偏差下界(英文)

在某种正则条件下,对Bayes估计尾概率收敛速度问题进行了讨论。利用似然理论方法得到了Bayes估计的中偏差下界,从而改善了Bahadur型的收敛结果。

一种高精度的频率估计算法研究

文章提出了一种基于DFT插值的频率估计算法,充分利用峰值及其左右谱线的幅度信息与相位信息进行估计。理论分析证明其是无偏估计,仿真结果表明该算法的性能接近修正克劳美罗限(MCRB),而且工作的信噪比门限低。该算法复杂度低,运算量小,便于实现和应用。

基于机会约束规划的机会阵雷达功率资源管理算法

研究了在进行多目标跟踪时机会数字阵列雷达(opportunistic digital array radar,ODAR)的功率资源管理问题。针对复杂多变的环境和未知的目标信息所导致的不确定性,建立了基于随机和模糊机会约束规划(chance-constraint programming,CCP)的多目标稳健功率资源管理模型。模型引入随机变量表征雷达总发射功率,引入模糊变量表征每个目标的RCS,以贝叶斯克拉美罗界(Bayesian Cramer Rao lower bound,BCRLB)作为目标跟踪精度的衡量标准,将随机模拟和模糊模拟都嵌入到遗传算法(genetic algorithm,GA)当中,从而预测出下一时刻满足给定置信水平的各目标最优的功率分配,然后根据求解出来的功率分配情况,利用无迹卡尔曼滤波器(unscented Kalman filter,UKF)进行目标跟踪。最后,通过仿真实验验证了算法的有效性和稳定性。

基于多目标跟踪的相控阵雷达波束和驻留时间联合分配方法

相控阵雷达能同时发射多个波束,其波束指向灵活,且能实现无惯性的快速扫描。为了使相控阵雷达用尽量少的系统资源去实现多个目标的同时跟踪,该文提出了一种基于多目标跟踪的波束和驻留时间联合分配方法。该方法通过建立和求解一个在各目标跟踪精度满足一定要求的前提下,最小化总波束驻留时间的非凸优化数学问题来实现资源的联合分配。贝叶斯克拉美罗界(BCRLB)为目标状态估计的误差提供了一个下界,该文推导了带有资源参数变量的BCRLB并将它作为跟踪性能的准则。随后针对上述非凸优化问题,该文提出一个先确立波束指向再分配驻留时间的两步分解算法。最后,根据资源分配结果,采用粒子滤波算法实现了多目标跟踪。仿真结果证明,和平均分配资源的固定操作方式相比,该文方法不仅能节约系统资源而且能保证坏目标的跟踪性能,体现了该文方法的有效性。

基于BP神经网络的相控阵雷达多目标跟踪时间资源优化分配方法

针对相控阵雷达多目标跟踪下的威胁度等级不同,以目标位置估计的贝叶斯克拉美罗下界(Bayesian Cramer-Rao lower bound,BCRLB)为分配准则,本文建立了一种基于威胁度的多目标跟踪时间资源分配优化模型,该模型以威胁度为基准将待跟踪目标分为两类,不同类别采用不同的时间资源分配方法。由于该模型及优化算法运行耗时巨大,该文还提出了一种基于反向传播(Back propagation,BP)神经网络的多目标跟踪时间资源拟合方法。计算机仿真表明,该模型及方法可以使各目标跟踪维持最佳状态,同时BP神经网络耗时降低2000多倍。

无线传感器网络中一种改进的分布式加权多维尺度定位算法

本文在无线传感器网络单跳定位误差分析的基础上,分析了多跳节点定位误差的特性,并据此提出针对分布式加权多维尺度定位(Distributed Weighted Multidimensional Scaling,dwMDS)的权值优化算法。在无法获知参考点确切误差的情况下,利用分析出来的克拉美劳下限代替参考点误差并与距离测量误差合并,更准确的反映了多跳定位中的点与点之间的误差,从而有助于设计更优化的权值。仿真结果表明,使用优化权值改进的算法得到的节点定位误差明显减小。

Time resource management of OAR based on fuzzy logic priority for multiple target tracking

For coping with the multiple target tracking in the presence of complex time-varying environments and unknown target information, a time resource management scheme based on chance-constraint programming（CCP） employing fuzzy logic priority is proposed for opportunistic array radar（OAR）. In this scheme,the total beam illuminating time is minimized by effective time resource allocation so that the desired tracking performance is achieved. Meanwhile, owing to the randomness of radar cross section（RCS）, the CCP is used to balance tracking accuracy and time resource conditioned on the specified confidence level. The adaptive fuzzy logic prioritization, imitating the human decision-making process for ranking radar targets, can realize the full potential of radar. The Bayesian Crame ′r-Rao lower bound（BCRLB） provides us with a low bound of localization estimation root-mean-square error（RMSE）, and equally important, it can be calculated predictively. Consequently, it is employed as an optimization criterion for the time resource allocation scheme. The stochastic simulation is integrated into the genetic algorithm（GA） to compose a hybrid intelligent optimization algorithm to solve the CCP optimization problem. The simulation results show that the time resource is saved strikingly and the radar performance is also improved.