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 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.

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.

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.

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.

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.

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.

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.