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.

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.

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 plotless density estimator with a Norton-Rice distribution for ordered distances

A Norton-Rice distribution(NRD)is a versatile,flexible distribution for k ordered distances from a random location to the k nearest objects.In a context of plotless density estimation(PDE)with n randomly chosen sample locations,and distances measured to the k=6 nearest objects,the NRD provided a good fit to distance data from seven populations with a census of forest tree stem locations.More importantly,the three parameters of a NRD followed a simple trend with the order(1,…,6)of observed distances.The trend is quantified and exploited in a proposed new PDE through a joint maximum likelihood estimation of the NRD parameters expressed as a functions of distance order.In simulated probability sampling from the seven populations,the proposed PDE had the lowest overall bias with a good performance potential when compared to three alternative PDEs.However,absolute bias increased by 0.8 percentage points when sample size decreased from 20 to 10.In terms of root mean squared error(RMSE),the new proposed estimator was at par with an estimator published in Ecology when this study was wrapping up,but otherwise superior to the remaining two investigated PDEs.Coverage of nominal 95%confidence intervals averaged 0.94 for the new proposed estimators and 0.90,0.96,and 0.90 for the comparison PDEs.Despite tangible improvements in PDEs over the last decades,a globally least biased PDE remains elusive.

Cramer-Rao bound and signal-to-noise ratio gain in distributed coherent aperture radar

This paper studies the estimation performance of the coherent processing parameter （CPP）, including time delay differences and phase synchronization errors among different apertures of the distributed coherent aperture radar （DCAR）. Firstly, three architectures of signal processing in the DCAR are introduced. Secondly, the closed-form Cramer-Rao bound （CRB） of the CPP estimation is derived and compared. Then, the closed-form CRB is verified by numerical simulations. Finally, when the next generation radar works in a fully coherent mode, the closed-form signal-to-noise ratio （SNR） gain of the three architectures is presented.

Demographics, societal aging, and meat consumption in China

Drawn on the data collected by surveying 1 340 urban households from six cities in China, this paper estimates the impacts of demographic structure and population aging on household meat consumption, by jointly considering meat consumed at home and away from home. Based on the trajectories of population, a simple simulation on meat demand trend in China is conducted subsequently. The results suggest： 1） Meat consumed away from home averagely accounts for near 30% of household total meat consumption in terms of quantity, so that its omission likely leads to a significant underestimate of total meat consumption and misunderstanding the driving forces; 2） population aging significantly and negatively affects per capita meat consumption, suggesting that the expected meat demand in China without considering population aging will be overestimated. The findings from this study have important implications for better understanding the relative issues on China＇s meat consumption under the situation of population aging.

Enhanced multi-baseline unscented Kalman filtering phase unwrapping algorithm

This paper presents an enhanced multi-baseline phase unwrapping algorithm by combining an unscented Kalman filter with an enhanced joint phase gradient estimator based on the amended matrix pencil model, and an optimal path-following strategy based on phase quality estimate function. The enhanced joint phase gradient estimator can accurately and effectively extract the phase gradient information of wrapped pixels from noisy interferograms, which greatly increases the performances of the proposed method. The optimal path-following strategy ensures that the proposed algorithm simultaneously performs noise suppression and phase unwrapping along the pixels with high-reliance to the pixels with low-reliance. Accordingly, the proposed algorithm can be predicted to obtain better results, with respect to some other algorithms, as will be demonstrated by the results obtained from synthetic data.

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.

Joint estimation of PM_(2.5) and O_(3) over China using a knowledge-informed neural network

China has currently entered a critical stage of coordinated control of fine particulate matter(PM_(2.5))and ozone(O_(3)),it is thus of tremendous value to accurately acquire high-resolution PM_(2.5) and O_(3) data.In contrast to traditional studies that usually separately estimate PM_(2.5) and O_(3),this study proposes a knowledge-informed neural network model for their joint estimation,in which satellite observations,reanalysis data,and ground station measurements are used.The neural network architecture is designed with the shared and specific inputs,the PM_(2.5)-O_(3) interaction module,and the weighted loss function,which introduce the prior knowledge of PM_(2.5) and O_(3) into neural network modeling.Cross-validation(CV)results indicate that the inclusion of prior knowledge can improve the estimation accuracy,with R^(2) increasing from 0.872 to 0.911 and from 0.906 to 0.937 for PM_(2.5) and O_(3) estimation under samplebased CV,respectively.In addition,the proposed joint estimation model achieves comparable performance with the separate estimation model,but with higher efficiency.Mapping results of PM_(2.5) and O_(3) derived by the proposed model have demonstrated interesting findings in the spatial and temporal trends and variations over China.

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.

A PILOT-ASSISTED NON-CYCLIC PREFIXED MIMO-SCFDE SYSTEM

A non-Cyclic Prefixed Multiple-Input Multiple-Output Single-Carrier Frequency-Domain Equalization (non-CP MIMO-SCFDE) system based on a recursive algorithm of Joint Channel Es- timation and Data Detection (recursive-JCEDD) is proposed in this paper. Unlike the traditional CP MIMO-SCFDE system, the transmitted block of the proposed system is designed in the way that block-type pilot sequences and Single-Carrier (SC) information sequences have been arranged alter- nately without any cyclic prefix before each SC information sequence. Moreover, a recursive-JCEDD algorithm based on interference cancellation is proposed for the corresponding receivers. Simulation results show that the Bit Error Rate (BER) of the proposed system based on the recursive-JCEDD algorithm is lower than traditional CP MIMO-SCFDE or MIMO-OFDM with channel estimation for more than 0.5 dB.

Joint parameter estimation of DOD/DOA/polarization for bistatic MIMO radar

In the paper,polarization-sensitive array is exploited at the receiver of multiple input multiple output （MIMO） radar system,a novel method is proposed for joint estimation of direction of departure （DOD）,direction of arrival （DOA） and polarization parameters for bistatic MIMO radars.A signal model of polarimetric MIMO radar is developed,and the multi-parameter estimation algorithm for target localization is described by exploiting polarization array processing and the invariance property in both transmitter array and receiver array.By making use of polarization diversity techniques,the proposed method has advantages over traditional localization algorithms for bistatic MIMO radar.Simulations show that the performance of DOD and DOA estimation is greatly enhanced when different states of polarization of echoes is fully utilized.Especially,when two targets are closely spaced and cannot be well separated in spatial domain,the estimation resolution of traditional algorithms will be greatly degraded.While the proposed algorithm can work well and achieve high-resolution identification and accurate localization of multiple targets.

Compressed sensing-based structured joint channel estimation in a multi-user massive MIMO system

Acquisition of accurate channel state information （CSI） at transmitters results in a huge pilot overhead in massive multiple input multiple output （MIMO） systems due to the large number of antennas in the base station （BS）. To reduce the overwhelming pilot overhead in such systems, a structured joint channel estimation scheme employing compressed sensing （CS） theory is proposed. Specifically, the channel sparsity in the angular domain due to the practical scattering environment is analyzed, where common sparsity and individual sparsity structures among geographically neighboring users exist in multi-user massive MIMO systems. Then, by equipping each user with multiple antennas, the pilot overhead can be alleviated in the framework of CS and the channel estimation quality can be improved. Moreover, a structured joint matching pursuit （SJMP） algorithm at the BS is proposed to jointly estimate the channel of users with reduced pilot overhead. Furthermore, the probability upper bound of common support recovery and the upper bound of channel estimation quality using the proposed SJMP algorithm are derived. Simulation results demonstrate that the proposed SJMP algorithm can achieve a higher system performance than those of existing algorithms in terms of pilot overhead and achievable rate.

Joint channel and carrier frequency offset estimation for multi-user MIMO-OFDM uplink transmissions

This article proposes a new algorithm of joint channel and carrier frequency-offset OCCFO） estimation for multi-user multi-input and multi-output orthogonal frequency division multiplexing （MIMO-OFDM） systems. A least square （LS） channel estimation and a carrier frequency offset （CFO） correlation estimation are combined in this contribution. CFOs are generally estimated using training sequences in a special synchronization timeslot. In this contribution, CFO estimation is further improved by taking advantages of channel estimation based on pilot symbols in traffic timeslots. The CFOs can be first obtained from the primary channel estimation. And then, with the knowledge of the CFOs estimated, channel estimation can be enhanced greatly. Computer simulation results indicate that the proposed JCCFO scheme is of good performance. Besides, the computational complexity is low.

Time synchronization and ranging under unknown positions and velocities

The process to achieve time synchronization and ranging for a network of mobile nodes is raising a concern among researchers, and hence a variety of joint time synchronization and ranging algorithms have been proposed in recent years. However, few of them handle the case of all-node motion under unknown positions and velocities. This study addresses the problem of determining ranging and time synchronization for a group of nodes moving within a local area. First, we examined several models of clock discrepancy and synchronous two-way ranging. Based upon these models, we present a solution for time synchronization with known positions and velocities. Next, we propose a functional model that jointly estimates the clock skew, clock offset, and time of flight in the absence of a priori knowledge for a pair of mobile nodes. Then, we extend this model to a network-wide time synchronization scheme by way of a global least square estimator. We also discuss the advantages and disadvantages of our model compared to the existing algorithms, and we provide some applicable scenarios as well. Finally, we show that the simulation results verify the validity of our analysis.

Time-varying latent model for longitudinal data with informative observation and terminal event times

Longitudinal data often occur in follow-up studies, and in many situations, there may exist informative observation times and a dependent terminal event such as death that stops the follow-up. We propose a semiparametric mixed effect model with time-varying latent effects in the analysis of longitudinal data with informative observation times and a dependent terminal event. Estimating equation approaches are developed for parameter estimation, and asymptotic properties of the resulting estimators are established. The finite sample behavior of the proposed estimators is evaluated through simulation studies, and an application to a bladder cancer study is provided.