Constrained least squares algorithm for channel vector estimation in 2-D RAKE receiver

Based on the fact that the variation of tile direction of arrival (DOA) isslower than that of the channel fading, the steering vector of the desired signal is estimatedfirstly using a subspace decomposition method and then a constrained condition is configured.Traffic signals are further employed to estimate the channel vector based on the constrained leastsquares criterion. We use the iterative least squares with projection (ILSP) algorithm initializedby the pilot to get the estimation. The accuracy of channel estimation and symbol detection can beprogressively increased through the iteration procedure of the ILSP algorithm. Simulation resultsdemonstrate that the proposed algorithm improves the system performance effectively compared withthe conventional 2-D RAKE receiver.

Stability of the MGS-like elimination method for equality constrained least squares problems

This paper proves that the weighting method via modified Gram-Schmidt（MGS） for solving the equality constrained least squares problem in the limit is equivalent to the direct elimination method via MGS（MGS-elimination method）. By virtue of this equivalence, the backward and forward roundoff error analysis of the MGS-elimination method is proved. Numerical experiments are provided to verify the results.

A CONSTRAINED LEAST SQUARES FITTING TECHNIQUE FOR ARMA MODELING

Several ARMA modeling approaches are addressed. In these methods only part of a correlation sequence is employed for estimating parameters. It is satisfying, if the given correlation sequence is of real ARMA, since an ARMA process can be completely determined by part of its correlation se -quence. But for the case of a measured correlation sequence the whole sequence may be used to reduce the effect of error on model parameter estimation. In addition, these methods now do not guarantee a nonnegative spectral estimate. In view of the above-mentioned fact, a constrained least squares fitting technique is proposed which utilizes the whole measured correlation sequence and guarantees a nonnegative spectral estimate.

Constrained Weighted Least Squares Location Algorithm Using Received Signal Strength Measurements

Determine the location of a target has gained considerable interest over the past few years. The Received Signal Strength(RSS) measurements and Differential RSS(DRSS) measurements can be converted to distance or distance ratio estimates for constructing a set of linear equations. Based on these linear equations, a constrained weighted least Squares(CWLS) algorithm for target localization is derived. In addition, an iterative technique based on Newton's method is utilized to give a solution. The covariance and bias of the CWLS algorithm is derived using perturbation analysis. Simulation shows that the proposed estimator achieves better performance than existing algorithms with reasonable complexity.

Constrained total least squares algorithm for passive location based on bearing-only measurements

The constrained total least squares algorithm for the passive location is presented based on the bearing-only measurements in this paper. By this algorithm the non-linear measurement equations are firstly transformed into linear equations and the effect of the measurement noise on the linear equation coefficients is analyzed, therefore the problem of the passive location can be considered as the problem of constrained total least squares, then the problem is changed into the optimized question without restraint which can be solved by the Newton algorithm, and finally the analysis of the location accuracy is given. The simulation results prove that the new algorithm is effective and practicable.

Damage Identification under Incomplete Mode Shape Data Using Optimization Technique Based on Generalized Flexibility Matrix

A generalized flexibility–based objective function utilized for structure damage identification is constructed for solving the constrained nonlinear least squares optimized problem. To begin with, the generalized flexibility matrix (GFM) proposed to solve the damage identification problem is recalled and a modal expansion method is introduced. Next, the objective function for iterative optimization process based on the GFM is formulated, and the Trust-Region algorithm is utilized to obtain the solution of the optimization problem for multiple damage cases. And then for computing the objective function gradient, the sensitivity analysis regarding design variables is derived. In addition, due to the spatial incompleteness, the influence of stiffness reduction and incomplete modal measurement data is discussed by means of two numerical examples with several damage cases. Finally, based on the computational results, it is evident that the presented approach provides good validity and reliability for the large and complicated engineering structures.

The structured total least squares algorithm research for passive location based on angle information

Based on the constrained total least squares （CTLS） passive location algorithm with bearing-only measurements, in this paper, the same passive location problem is transformed into the structured total least squares （STLS） problem.The solution of the STLS problem for passive location can be obtained using the inverse iteration method.It also expatiates that both the STLS algorithm and the CTLS algorithm have the same location mean squares error under certain condition.Finally, the article presents a kind of location and tracking algorithm for moving target by combining STLS location algorithm with Kalman filter （KF）.The efficiency and superiority of the proposed algorithms can be confirmed by computer simulation results.

Semidefinite programming approach for TDOA/GROA based source localization

Time-differences-of-arrival （TDOA） and gain-ratios-of- arrival （GROA） measurements are used to determine the passive source location. Based on the measurement models, the con- strained weighted least squares （CWLS） estimator is presented. Due to the nonconvex nature of the CWLS problem, it is difficult to obtain its globally optimal solution. However, according to the semidefinite relaxation, the CWLS problem can be relaxed as a convex semidefinite programming problem （SDP）, which can be solved by using modern convex optimization algorithms. Moreover, this relaxation can be proved to be tight, i.e., the SDP solves the relaxed CWLS problem, and this hence guarantees the good per- formance of the proposed method. Furthermore, this method is extended to solve the localization problem with sensor position errors. Simulation results corroborate the theoretical results and the good performance of the proposed method.

Assessing tree crown fire damage integrating linear spectral mixture analysis and supervised machine learning on Sentinel-2 imagery

Crown fire damage is a mixture of three principal fire-related components:charred material,scorched foliage,and unaltered green canopy.This study estimated the abundance of these physical alterations in two immediate post-fire Mediterranean forest contexts(Portugal and Italy)by applying linear spectral mixture analysis(LSMA)on Sentinel-2 imagery.The tree crowns fire damage was subsequently mapped,integrating fractional abundance information in a random forest(RF)algorithm,comparing the accuracy resulting from the adoption of generic or image spectral libraries as the primary investigative goal.Although image-derived endmembers resulted in more effectiveness in terms of fire-related components abundance quantification(LMSAderived RMSE<0.1),the F-scores always were≥90%whether generic endmembers or image endmembers derived information was employed.The environmental heterogeneity of the two study areas affected the fire severity gradients,with a prevalence of the charred(PT)(45–46%)and green class(IT)(44–53%).Post-fire temporal monitoring was initialized by applying the proposed strategies,and the preliminary results showed a positive recovery trend in forest vegetation from the first year following the fire event,with a reduced charcoal predominance and an increasing proportion of green components.