Low-Complexity Joint Channel Estimation and Symbol Detection for OFDMA Systems

In this paper,we propose a joint channel estimation and symbol detection(JCESD)algorithm relying on message-passing algorithms(MPA)for orthogonal frequency division multiple access(OFDMA)systems.The channel estimation and symbol detection leverage the framework of expectation propagation(EP)and belief propagation(BP)with the aid of Gaussian approximation,respectively.Furthermore,to reduce the computation complexity involved in channel estimation,the matrix inversion is transformed into a series of diagonal matrix inversions through the Sherman-Morrison formula.Simulation experiments show that the proposed algorithm can reduce the pilot overhead by about 50%,compared with the traditional linear minimum mean square error(LMMSE)algorithm,and can approach to the bit error rate(BER)performance bound of perfectly known channel state information within 0.1 dB.

Joint 2D DOA and Doppler frequency estimation for L-shaped array using compressive sensing

A joint two-dimensional(2D)direction-of-arrival(DOA)and radial Doppler frequency estimation method for the L-shaped array is proposed in this paper based on the compressive sensing(CS)framework.Revised from the conventional CS-based methods where the joint spatial-temporal parameters are characterized in one large scale matrix,three smaller scale matrices with independent azimuth,elevation and Doppler frequency are introduced adopting a separable observation model.Afterwards,the estimation is achieved by L1-norm minimization and the Bayesian CS algorithm.In addition,under the L-shaped array topology,the azimuth and elevation are separated yet coupled to the same radial Doppler frequency.Hence,the pair matching problem is solved with the aid of the radial Doppler frequency.Finally,numerical simulations corroborate the feasibility and validity of the proposed algorithm.

Cramer-Rao bound of joint estimation of target location and velocity for coherent MIMO radar

The optimal estimation performance of target parameters is studied. First, the general form of Cramer-Rao bound （CRB） for joint estimation of target location and velocity is derived for coherent multiple input multiple output （MIMO） radars. To gain some insight into the behavior of the CRB, the CRB with a set of given orthogonal waveforms is studied as a specific case. Second, a maximum likelihood （ML） estimation algorithm is proposed. The mean square error （MSE） of the ML estimation of target location and velocity is obtained by Monte Carlo simulation and it approaches CRB in the high signal-to-noise ratio （SNR） region.

Computationally Efficient MUSIC-Based Algorithm for Joint Direction of Arrival (DOA) and Doppler Frequency Estimation in Monostatic MIMO Radar

The problem of joint direction of arrival (DOA) and Doppler frequency estimation in monostatic multiple-input multiple-output (MIMO) radar is studied and a computationally efficient multiple signal classification (CE-MUSIC) algorithm is proposed.Conventional MUSIC algorithm for joint DOA and Doppler frequency estimation requires a large computational cost due to the two dimensional (2D) spectral peak searching.Aiming at this shortcoming,the proposed CE-MUSIC algorithm firstly uses a reduced-dimension transformation to reduce the subspace dimension and then obtains the estimates of DOA and Doppler frequency with only one-dimensional (1D) search.The proposed CE-MUSIC algorithm has much lower computational complexity and very close estimation performance when compared to conventional 2D-MUSIC algorithm.Furthermore,it outperforms estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm.Meanwhile,the mean squared error (MSE) and Cramer-Rao bound (CRB) of joint DOA and Doppler frequency estimation are derived.Detailed simulation results illustrate the validity and improvement of the proposed algorithm.

ADC-GERT network parameter estimation model for mission effectiveness of joint operation system

Effectiveness evaluation of the joint operation system is an important basis for the demonstration and development of weapon equipment.With the consideration that existing models of system effectiveness evaluation seldom describe the structural relationship among equipment clearly as well as reflect the dynamic,the analog-to-digital converter-graphical evaluation and review technique(ADC-GERT)network parameter estimation model is proposed based on the ADC model and the joint operation system structure.Firstly,analysis of the joint operation system structure and operation process is conducted to build the GERT network,where equipment subsystems are nodes and activities are directed arches.Then the mission effectiveness of equipment subsystems is calculated by the ADC model.The probability transfer parameters are modified by the mission effectiveness of equipment subsystems based on the Bayesian theorem,with the ADC-GERT network parameter estimation model constructed.Finally,a case study is used to validate the efficiency and dynamic of the ADC-GERT network parameter estimation model.

An improved bidirectional generative adversarial network model for multivariate estimation of correlated and imbalanced tunnel construction parameters

Estimation of construction parameters is crucial for optimizing tunnel construction schedule.Due to the influence of routine activities and occasional risk events,these parameters are usually correlated and imbalanced.To solve this issue,an improved bidirectional generative adversarial network(BiGAN)model with a joint discriminator structure and zero-centered gradient penalty(0-GP)is proposed.In this model,in order to improve the capability of original BiGAN in learning imbalanced parameters,the joint discriminator separately discriminates the routine activities and risk event durations to balance their influence weights.Then,the self-attention mechanism is embedded so that the discriminator can pay more attention to the imbalanced parameters.Finally,the 0-GP is adapted for the loss of the discrimi-nator to improve its convergence and stability.A case study of a tunnel in China shows that the improved BiGAN can obtain parameter estimates consistent with the classical Gauss mixture model,without the need of tedious and complex correlation analysis.The proposed joint discriminator can increase the ability of BiGAN in estimating imbalanced construction parameters,and the 0-GP can ensure the stability and convergence of the model.

Joint synchronization estimation based on genetic algorithm for OFDM/OQAM systems

This paper investigates the problem of synchronization for offset quadrature amplitude modulation based orthogonal frequency division multiplexing(OFDM/OQAM) systems based on the genetic algorithm. In order to increase the spectrum efficiency,an improved preamble structure without guard symbols is derived at first. On this basis, instead of deriving the log likelihood function of power spectral density, joint estimation of the symbol timing offset and carrier frequency offset based on the preamble proposed is formulated into a bivariate optimization problem. After that, an improved genetic algorithm is used to find its global optimum solution. Conclusions can be drawn from simulation results that the proposed method has advantages in the joint estimation of synchronization.

Joint DOA and polarization estimation for unequal power sources based on reconstructed noise subspace

In most literature about joint direction of arrival（DOA） and polarization estimation, the case that sources possess different power levels is seldom discussed. However, this case exists widely in practical applications, especially in passive radar systems. In this paper, we propose a joint DOA and polarization estimation method for unequal power sources based on the reconstructed noise subspace. The invariance property of noise subspace（IPNS） to power of sources has been proved an effective method to estimate DOA of unequal power sources. We develop the IPNS method for joint DOA and polarization estimation based on a dual polarized array. Moreover, we propose an improved IPNS method based on the reconstructed noise subspace, which has higher resolution probability than the IPNS method. It is theoretically proved that the IPNS to power of sources is still valid when the eigenvalues of the noise subspace are changed artificially. Simulation results show that the resolution probability of the proposed method is enhanced compared with the methods based on the IPNS and the polarimetric multiple signal classification（MUSIC） method. Meanwhile, the proposed method has approximately the same estimation accuracy as the IPNS method for the weak source.

LMMSE-based SAGE channel estimation and data detection joint algorithm for MIMO-OFDM system

A new channel estimation and data detection joint algorithm is proposed for multi-input multi-output （MIMO） - orthogonal frequency division multiplexing （OFDM） system using linear minimum mean square error （LMMSE）- based space-alternating generalized expectation-maximization （SAGE） algorithm. In the proposed algorithm, every sub-frame of the MIMO-OFDM system is divided into some OFDM sub-blocks and the LMMSE-based SAGE algorithm in each sub-block is used. At the head of each sub-flame, we insert training symbols which are used in the initial estimation at the beginning. Channel estimation of the previous sub-block is applied to the initial estimation in the current sub-block by the maximum-likelihood （ML） detection to update channel estimatjon and data detection by iteration until converge. Then all the sub-blocks can be finished in turn. Simulation results show that the proposed algorithm can improve the bit error rate （BER） performance.

A novel adaptive joint power control algorithm with channel estimation in a CDMA cellular system

Joint power control has advantages of multi-user detection and power control; and it can combat the multi-access interference and the near-far problem. A novel adaptive joint power control algorithm with channel estimation in a CDMA cellular system was designed. Simulation results show that the algorithm can control the power not only quickly but also precisely with a time change. The method is useful for increasing system capacity.

A method of Robust low-angle target height and compound reflection coefficient joint estimation

It is always a challenging issue for radar systems to estimate the height of a low-angle target in the multipath propagation environment.The highly deterministic maximum likelihood estimator has a high accuracy,but the errors of the ground reflection coefficient and the reflecting surface height have serious influence on the method.In this paper,a robust es-timation method with less computation burden is proposed based on the compound reflection coefficient multipath model for low-angle targets.The compound reflection coefficient is es-timated from the received data of the array and then a one-di-mension generalized steering vector is constructed to estimate the target height.The algorithm is robust to the reflecting sur-face height error and the ground reflection coefficient error.Fi-nally,the experiment and simulation results demonstrate the validity of the proposed method.

A variable structure passivity control method for elastic joint robots based on cascaded high-order state estimation

Passivity-based controllers are widely used to facilitate physical interaction between humans and elastic joint robots,as they enhance the stability of the interaction system.However,the joint position tracking performance can be limited by the structures of these controllers when the system is faced with uncertainties and rough high-order system state measurements(such as joint accelerations and jerks).This study presents a variable structure passivity(VSP)control method for joint position tracking of elastic joint robots,which combines the advantages of passive control and variable structure control.This method ensures the tracking error converges in a finite time,even when the system faces uncertainties.The method also preserves the passivity of the system.Moreover,a cascaded observer,called CHOSSO,is also proposed to accurately estimate high-order system states,relying only on position and velocity signals.This observer allows independent implementation of disturbance compensation in the acceleration and jerk estimation channels.In particular,the observer has an enhanced ability to handle fast time-varying disturbances in physical human-robot interaction.The effectiveness of the proposed method is verified through simulations and experiments on a lower limb rehabilitation robot equipped with elastic joints.

A Novel Training Sequence Applied to DCS-Based Channel Estimation

Studies have indicated that the distributed compressed sensing based(DCSbased) channel estimation can decrease the length of the reference signals effectively. In block transmission, a unique word(UW) can be used as a cyclic prefix and reference signal. However, the DCS-based channel estimation requires diversity sequences instead of UW. In this paper, we proposed a novel method that employs a training sequence(TS) whose duration time is slightly longer than the maximum delay spread time. Based on proposed TS, the DCS approach perform perfectly in multipath channel estimation. Meanwhile, a cyclic prefix construct could be formed, which reduces the complexity of the frequency domain equalization(FDE) directly. Simulation results demonstrate that, by using the method of simultaneous orthogonal matching pursuit(SOMP), the required channel overhead has been reduced thanks to the proposed TS.

Wind disturbance rejection control based on the dynamic parameters estimation of quadrotors UAV

The turbulence or gust in quadrotor flight environment causes drastic changes in the unmanned aerial vehicle(UAV)aerodynamic parameters.Especially,rotor thrust coefficient and reaction torque coefficient of the UAV encounter uncertainty fluctuation,which may undermine the control performance.A real-time estimation strategy for these aerodynamic parameters is proposed to improve the identification on the disturbance.First,the unscented Kalman filter(UKF)algorithm is used to estimate the UAV states and aerodynamic parameters.Then,a double-loop structure,consisting of position and attitude,is designed for the trajectory tracking control.In the outer loop,a proportional-derivative controller is adopted to carry out position tracking and provide Euler angle references for the inner loop,called attitude controller.Moreover,the attitude controller is designed using an inverse dynamic technique.The main contribution of this study is to propose a joint estimation on the aerodynamic parameters with wind disturbance as well as the UAV states.This strategy plays an important role in refining time-varying parameters of wind disturbance.A number of simulations are executed to verify the effectiveness of the proposed method.

Research on Rainfall Estimation Based on Improved Kalman Filter Algorithm

In order to solve the rainfall estimation error caused by various noise factors such as clutter,super refraction,and raindrops during the detection process of Doppler weather radar.This paper proposes to improve the rainfall estimation model of radar combined with rain gauge which calibrated by common Kalman filter.After data preprocessing,the radar data should be classified according to the precipitation intensity.And then,they are respectively substituted into the improved filter for calibration.The state noise variance Q(k)and the measurement noise variance R(k)can be adaptively calculated and updated according to the input observation data during this process.Then the optimal parameter value of each type of precipitation intensity can be obtained.The state noise variance Q(k)and the measurement noise variance R(k)could be assigned optimal values when filtering the remaining data.This rainfall estimation based on semiadaptive Kalman filter calibration not only improves the accuracy of rainfall estimation,but also greatly reduces the amount of calculation.It avoids errors caused by repeated calculations,and improves the efficiency of the rainfall estimation at the same time.

低复杂度的大规模MIMO分布式信道估计

针对大规模多输入多输出(multiple-input multiple-output,MIMO)系统传统信道矩阵获取方式导频开销大、计算复杂度高的问题,提出了一种低复杂度的二阶段分布式信道估计方案。该方案的初始阶段在基站侧采用传统压缩感知算法恢复信道矩阵,第2阶段在用户端利用信道的时间相关性,将大规模MIMO的角度域信道分解为密集部分和稀疏部分,并分别估计以实现连续信道追踪。稀疏部分信道通过所提的分布式自适应弱匹配追踪(distributed adaptive weak matching pursuit,DAWMP)算法,利用子信道的联合稀疏性进行多维重建。相比于线性最小均方误差(linear minimum mean square error,LMMSE)算法,所提方案的信道分解策略有效减少了在用户端进行信道估计的计算复杂度。仿真结果表明,所提算法与经典压缩感知信道估计算法相比,计算复杂度降低了约33%,算法性能提升了约0.5 dB。

联合比估计法在敏感性问题调查中的应用

文章基于定性特征敏感性随机化模型——分层抽样下的Warner模型,运用联合比估计法对敏感性问题调查比例的估计量及估计误差进行了理论推导。通过效率比较得出:当各层样本量较大、比估计有效时,联合比估计法的精度优于分层估计法的精度。经调查实例验证,上述结论正确。

基于输入−状态联合估计的漂浮式风机塔架结构载荷反演及状态分析

[目的]旨在研究波浪作用下漂浮式风机塔架的结构载荷反演方法及动力响应状态。[方法]以半潜型风机为研究对象,通过时域仿真模拟测量数据,并且基于漂浮式风机的频域仿真模型计算塔架的质量归一化模态,将二者作为输入−状态联合估计(joint input-state estimation,JIS)算法的输入,对漂浮式风机塔架开展结构载荷反演以及实时状态分析。[结果]结果表明:基于JIS算法,以塔架节点的仿真数据作为算法的输入,估计的塔根载荷以及塔架节点响应与仿真结果具有良好的一致性,实现了高精度的反演。[结论]反演结果验证了JIS算法的有效性,可以用于漂浮式风机的实时状态分析,保障漂浮式风机长期安全稳定运行。

L型阵水下目标方位和距离联合最大似然估计

空间目标定位是阵列信号处理领域中的研究热点,广泛应用于雷达、声纳、通信、射电天文、医学诊断、地震遥感等众多领域。传统的水下声源定位仅获取目标声源的深度和距离信息,却无法估计目标的方位信息。针对这一问题,在充分考虑水下声传播模型的基础上,利用L型阵列固有的结构特征,将最大似然估计技术应用到水下目标定位,提出一种基于L型水听器阵列的水下目标方位与距离联合最大似然估计算法。研究表明:最大似然估计法可有效突破传统波束形成分辨力“瑞利限”的限制,提高水下目标定位的分辨能力和精度。计算机仿真分析显示,在单目标和相干双目标的场景中,文中所提出的方法均具有更为尖锐的谱峰及更低的旁瓣水平,可对水下目标进行有效的方位角和距离联合估计。

基于自适应H_(∞)观测器的锂电池SOC与容量联合估计

为了提高锂电池SOC的估计精度,提出了一种基于自适应H_(∞)观测器的锂电池SOC与容量联合估计方法。基于锂电池的二阶RC等效电路模型,并考虑SOC和容量的耦合关系,将容量也作为系统的状态变量进行观测。设计了一种自适应H_(∞)观测器,其参数可随锂电池状态的变化自适应调整。由于在SOC估算时考虑了容量的影响,实现了SOC和容量的同时准确估计。实验结果表明:自适应H_(∞)观测器的估计精度高且鲁棒性强,电池的SOC平均估计误差始终保持在±0.43%以内,相比于EKF和H_(∞)观测器有更高的估计精度与稳定性。