Wideband source localization using a distributed acoustic vector-sensor array

We derive fast wideband algorithms, based on measurements of the acoustic intensity, for determining the bearings of a target using an acoustic vector sensor (AVS) situated in free space or on a reflecting boundary. We also obtain a lower bound on the mean-square angular error (MSAE) of such estimates. We then develop general closed-form weighted least-squares (WLS) and reweighted least-squares algorithms that compute the three-dimensional (3-D) location of a target whose bearing to a number of dispersed locations has been measured. We devise a scheme for adaptively choosing the weights for the WLS routine when measures of accuracy for the bearing estimates, such as the lower bound on the MSAE, are available. In addition, a measure of the potential estimation accuracy of a distributed system is developed based on a two-stage application of the Cramer-Rao bound. These 3-D results are quite independent of how bearing estimates are obtained. Naturally, the two parts of the paper are tied together by examining how well distributed arrays of AVSs located on the ground, seabed, and in free space can determine the 3-D position of a target The results are relevant to the localization of underwater and airborne sources using freely drifting, moored, or ground sensors. Numerical simulations illustrate the effectiveness of our estimators and the new potential performance measure.     

Accelerated penalized weighted least-squares and maximum likelihood algorithms for reconstructing transmission images from PET transmission data

We present penalized weighted least-squares (PWLS) and penalized maximum-likelihood (PML) methods for reconstructing transmission images from positron emission tomography transmission data. First, we view the problem of minimizing the weighted least-squares (WLS) and maximum likelihood objective functions as a sequence of nonnegative least-squares minimization problems. This viewpoint follows from using certain quadratic functions as surrogate functions for the WLS and maximum likelihood objective functions. Second, we construct surrogate functions for a class of penalty functions that yield closed form expressions for the iterates of the PWLS and PML algorithms. Due to the slow convergence of the PWLS and PML algorithms, accelerated versions of them are developed that are theoretically guaranteed to monotonically decrease their respective objective functions. In experiments using real phantom data, the PML images produced the most accurate attenuation correction factors. On the other hand, the PWLS images produced images with the highest levels of contrast for low-count data.     

基于到达角Kalman滤波的TDOA/AOA定位算法

基于Chan算法的TDOA/AOA定位算法是在Chan算法的信号到达时间差(TDOA)误差方程组里加上一个信号到达角(AOA)误差方程,利用加权最小二乘法(WLS)求解。其主要缺点是把移动台(MS)的横坐标、纵坐标与移动台到服务基站(BS)之间的距离作为3个相互独立的变量,忽略了3者之间的相关性。需要进行两次WLS计算,且最终的解为二值根。当AOA测量误差的方差不断增大时,对应的定位误差也随之增大。该文利用Kalman滤波算法对AOA的值进行估计,并将上述的3个变量简化为一个,只需一次WLS即可求得唯一解,减少了计算量,消除了根的模糊性。仿真结果表明,该方法简单,计算量小,有较高的定位精度和较好的稳健性。     

Weighted least squares near-equiripple approximation of variable fractional delay FIR filters

The weighted least squares (WLS) method is a well-known method for designing a finite impulse response (FIR) filter. And some authors have reported that if a suitable frequency response weighting function is used to design the filter, the WLS method can produce an equiripple result. However, the weighting function for minimax optimality of WLS design is hard to derive analytically. By an iterative method with an adjustable elaborately constructed weighting function, this idea is extended to design a near-equiripple variable fractional delay FIR filter. The proposed method is superior to the fixed-weighting WLS design in the peak absolute error by about 6.6874 dB. The algorithm converges very rapidly. From the simulation, it typically produces a design which is only about 1 dB away from the truly equiripple solution in two iterations and converges to within 0.0056 dB in eight iterations.     

Efficient 2-D based algorithms for WLS designs of 2-D FIR filters with arbitrary weighting functions

The impulse response coefficients of a two-dimensional (2-D) finite impulse response (FIR) filter naturally constitute a matrix. It has been shown by several researchers that, two-dimension (2-D) based algorithms that retain the natural matrix form of the 2-D filter’s coefficients are computationally much more efficient than the conventional one-dimension (1-D) based algorithms that rearrange the coefficient matrix into a vector. In this paper, two 2-D based algorithms are presented for the weighted least squares (WLS) design of quadrantally symmetric 2-D FIR filters with arbitrary weighting functions. Both algorithms are based on matrix iterative techniques with guaranteed convergence, and they solve the WLS design problems accurately and efficiently. The convergence rate, solution accuracy and design time of these proposed algorithms are demonstrated and compared with existing algorithms through two design examples.     

Regularized image reconstruction algorithms for positron emission tomography

We develop algorithms for obtaining regularized estimates of emission means in positron emission tomography. The first algorithm iteratively minimizes a penalized maximum-likelihood (PML) objective function. It is based on standard de-coupled surrogate functions for the ML objective function and de-coupled surrogate functions for a certain class of penalty functions. As desired, the PML algorithm guarantees nonnegative estimates and monotonically decreases the PML objective function with increasing iterations. The second algorithm is based on an iteration dependent, de-coupled penalty function that introduces smoothing while preserving edges. For the purpose of making comparisons, the MLEM algorithm and a penalized weighted least-squares algorithm were implemented. In experiments using synthetic data and real phantom data, it was found that, for a fixed level of background noise, the contrast in the images produced by the proposed algorithms was the most accurate.     

Nonparametric regression sinogram smoothing using a roughness-penalized Poisson likelihood objective function

We develop and investigate an approach to tomographic image reconstruction in which nonparametric regression using a roughness-penalized Poisson likelihood objective function is used to smooth each projection independently prior to reconstruction by unapodized filtered backprojection (FBP). As an added generalization, the roughness penalty is expressed in terms of a monotonic transform, known as the link function, of the projections. The approach is compared to shift-invariant projection filtering through the use of a Hanning window as well as to a related nonparametric regression approach that makes use of an objective function based on weighted least squares (WLS) rather than the Poisson likelihood. The approach is found to lead to improvements in resolution-noise tradeoffs over the Hanning filter as well as over the WLS approach. We also investigate the resolution and noise effects of three different link functions: the identity, square root, and logarithm links. The choice of link function is found to influence the resolution uniformity and isotropy properties of the reconstructed images. In particular, in the case of an idealized imaging system with intrinsically uniform and isotropic resolution, the choice of a square root link function yields the desirable outcome of essentially uniform and isotropic resolution in reconstructed images, with noise performance still superior to that of the Hanning filter as well as that of the WLS approach.     

Estimation of Q-factors and resonant frequencies

We estimate the quality factor Q and resonant frequency f/sub 0/ of a microwave cavity based on observations of a resonance curve on an equally spaced frequency grid. The observed resonance curve is the squared magnitude of an observed complex scattering parameter. We characterize the variance of the additive noise in the observed resonance curve parametrically. Based on this noise characterization, we estimate Q and f/sub 0/ and other associated model parameters using the method of weighted least squares (WLS). Based on asymptotic statistical theory, we also estimate the one-sigma uncertainty of Q and f/sub 0/. In a simulation study, the WLS method outperforms the 3-dB method and the Estin method. For the case of measured resonances, we show that the WLS method yields the most precise estimates for the resonant frequency and quality factor, especially for resonances that are undercoupled. Given that the resonance curve is sampled at a fixed number of equally spaced frequencies in the neighborhood of the resonant frequency, we determine the optimal frequency spacing in order to minimize the asymptotic standard deviation of the estimate of either Q or f/sub 0/.     

Generalized EM-Type Reconstruction Algorithms for Emission Tomography

We provide a general form for many reconstruction estimators of emission tomography. These estimators include Shepp and Vardi's maximum likelihood (ML) estimator, the quadratic weighted least squares (WLS) estimator, Anderson's WLS estimator, and Liu and Wang's multi-objective estimator, and others. We derive a generic update rule by constructing a surrogate function. This work is inspired by the ML-EM (EM, expectation maximization), where the latter naturally arises as a special case. A regularization with a specific form can also be incorporated by De Pierro's trick. We provide a general and quite different convergence proof compared with the proofs of the ML-EM and De Pierro. Theoretical analysis shows that the proposed algorithm monotonically decreases the cost function and automatically meets nonnegativity constraints. We have introduced a mechanism to provide monotonic, self-constraining, and convergent algorithms, from which some interesting existing and new algorithms can be derived. Simulation results illustrate the behavior of these algorithms in term of image quality and resolution-noise tradeoff.     

一种新的分布式MIMO雷达系统运动目标定位代数解算法

该文针对分布式MIMO雷达系统中的运动目标定位问题,以双基地距离(BR)及其变化率(BRR)作为观测量,提出一种基于多步加权最小二乘的代数解算法。算法共需要3步加权最小二乘估计。首先,在第1步加权最小二乘估计中,通过选取适当的辅助参数,将非线性的BR和BRR的观测方程进行伪线性化处理,从而得到目标位置和速度的粗略解;而后在后两步加权最小二乘估计中,利用目标位置参数和辅助参数之间的约束关系构建方程,从而得到目标位置和速度的精确估计。最后,推导了算法的理论误差,从理论上证明了算法可以达到克拉美罗界。在仿真实验中,将所提算法与现有算法进行了对比,验证了算法的优越性。     

一种加权最小熵的ISAR自聚焦算法

基于加权最小二乘估计(WLS)的最小方差准则,根据各个距离单元的相位方差的差异,该文提出了一种加权最小熵的ISAR自聚焦算法,利用加权熵建立代价函数,通过迭代算法估计误差相位以实现运动误差补偿。该算法具有较高的鲁棒性,相对于传统最小熵ISAR自聚焦算法,能够有效提高迭代的收敛速度,并且权值系数的应用可以有效降低杂波和噪声的影响,从而取得更好的聚焦效果。基于仿真数据和实测数据的实验验证了该算法的有效性。     

Improved weighted least squares algorithm for the design ofquadrature mirror filters

This paper presents an improved weighted least squares (WLS) algorithm for the design of quadrature mirror filters (QMFs), First, a new term is incorporated into the objective function that effectively prevents an optimization algorithm from producing suboptimal QMFs. These suboptimal QMFs exhibit a transition band anomaly; the frequency responses of the filters have large oscillatory components in the transition band. The new term can be applied to the WLS design of any FIR filter to prevent a similar transition band anomaly. Next, we present an algorithm to obtain the QMF coefficients that minimize the objective function incorporating the new term. The computational requirement of this algorithm is also briefly discussed. Last, we include a set of practical design rules for use with our algorithm. These rules simplify the design process by providing good estimation of the design parameters, such as the minimum filter length, to meet a given set of QMF specifications     

Asymptotic analysis of stochastic gradient-based adaptive filteringalgorithms with general cost functions

This paper presents an analysis of stochastic gradient-based adaptive algorithms with general cost functions. The analysis holds under mild assumptions on the inputs and the cost function. The method of analysis is based on an asymptotic analysis of fixed stepsize adaptive algorithms and gives almost sure results regarding the behavior of the parameter estimates, whereas previous stochastic analyses typically considered mean and mean square behavior. The parameter estimates are shown to enter a small neighborhood about the optimum value and remain there for a finite length of time. Furthermore, almost sure exponential bounds are given for the rate of convergence of the parameter estimates. The asymptotic distribution of the parameter estimates is shown to be Gaussian with mean equal to the optimum value and covariance matrix that depends on the input statistics. Specific adaptive algorithms that fall under the framework of this paper are signed error least mean square (LMS), dual sign LMS, quantized state LMS, least mean fourth, dead zone algorithms, momentum algorithms, and leaky LMS     

基于约束加权最小二乘的多站无源定位

针对传统加权最小二乘算法在噪声较大时会出现门限效应的问题。本文将约束加权最小二乘算法应用于多站无源定位,分别提出了基于多站时差定位的约束加权最小二乘算法以及基于时差频差联合定位的约束加权最小二乘算法。算法首先将非线性观测方程转化为两个伪线性方程,然后对伪线性方程加入限制条件,得到目标位置。仿真实验表明,与经典加权最小二乘算法及其改进算法相比,新算法在计算量增大不多的情况下扩展了适用范围,当噪声超过门限值时依然能获得较高的定位精度。新算法对噪声具有较强的鲁棒性,能有效克服门限效应带来的影响。      

Non-orthogonal joint diagonalization in the least-squares sensewith application in blind source separation

Approximate joint diagonalization of a set of matrices is an essential tool in many blind source separation (BSS) algorithms. A common measure of the attained diagonalization of the set is the weighted least-squares (WLS) criterion. However, most well-known algorithms are restricted to finding an orthogonal diagonalizing matrix, relying on a whitening phase for the nonorthogonal factor. Often, such an approach implies unbalanced weighting, which can result in degraded performance. We propose an iterative alternating-directions algorithm for minimizing the WLS criterion with respect to a general (not necessarily orthogonal) diagonalizing matrix. Under some mild assumptions, we prove weak convergence in the sense that the norm of parameters update is guaranteed to fall below any arbitrarily small threshold within a finite number of iterations. We distinguish between Hermitian and symmetrical problems. Using BSS simulations results, we demonstrate the improvement in estimating the mixing matrix, resulting from the relaxation of the orthogonality restriction     

Space-alternating generalized expectation-maximization algorithm

The expectation-maximization (EM) method can facilitate maximizing likelihood functions that arise in statistical estimation problems. In the classical EM paradigm, one iteratively maximizes the conditional log-likelihood of a single unobservable complete data space, rather than maximizing the intractable likelihood function for the measured or incomplete data. EM algorithms update all parameters simultaneously, which has two drawbacks: 1) slow convergence, and 2) difficult maximization steps due to coupling when smoothness penalties are used. The paper describes the space-alternating generalized EM (SAGE) method, which updates the parameters sequentially by alternating between several small hidden-data spaces defined by the algorithm designer. The authors prove that the sequence of estimates monotonically increases the penalized-likelihood objective, derive asymptotic convergence rates, and provide sufficient conditions for monotone convergence in norm. Two signal processing applications illustrate the method: estimation of superimposed signals in Gaussian noise, and image reconstruction from Poisson measurements. In both applications, the SAGE algorithms easily accommodate smoothness penalties and converge faster than the EM algorithms     

Efficient weighted least-squares algorithm for the design of FIRfilters

The weighted least-squares (WLS) technique has been widely used for the design of digital FIR filters. In the conventional WLS, the filter coefficients are obtained by performing a matrix inverse operation, which needs computation of O(N³). The authors present a new WLS algorithm that introduces an extra frequency response including implicitly the weight function. In the new algorithm, the filter coefficients can be solved just by a matrix vector multiplication. It reduces the computational complexity from O(N³ ) to O(N²)     

WCDMA系统中空时2D-RAKE接收机性能分析

分析了在WCDMA系统的基站中采用含智能天线的空时2D-RAKE接收机的结构和性能.在建立了WCDMA信道、信号模型的基础上,给出了输出误码率的表达式.提出了一种新的利用两个连续时隙的导频符号估计出的信道参数进行加权最小二乘意义下二次曲线内插的信道参数估计方法(WLS).仿真结果表明:WLS算法能很好地跟踪多径衰落信道的变化,提出的空时2D RAKE接收机的误码性能大大改善,而且在移动台移动速度较高时仍能正常工作.     

基于时差序列的多机无源定位算法研究

多站时差序列定位法是一种针对脉冲辐射源的无源定位方法,它是利用短时间内获得的大量时差信息进行定位。文中基于时差序列的定位体制,提出了2个实用的多机定位算法。在目标脉冲波束扫过我方飞行编队的短时间内,将载机位置近似为常值,从而可以使用能够瞬时定位的两步加权最小二乘算法,当测量误差比较大时,采用最大似然法进一步提高定位精度。仿真结果表明,文中提出的基于时差序列的多机定位算法能够达到较高的定位精度,具有很好的工程实用价值。     

Nonlinear image recovery with half-quadratic regularization

One popular method for the recovery of an ideal intensity image from corrupted or indirect measurements is regularization: minimize an objective function that enforces a roughness penalty in addition to coherence with the data. Linear estimates are relatively easy to compute but generally introduce systematic errors; for example, they are incapable of recovering discontinuities and other important image attributes. In contrast, nonlinear estimates are more accurate but are often far less accessible. This is particularly true when the objective function is nonconvex, and the distribution of each data component depends on many image components through a linear operator with broad support. Our approach is based on an auxiliary array and an extended objective function in which the original variables appear quadratically and the auxiliary variables are decoupled. Minimizing over the auxiliary array alone yields the original function so that the original image estimate can be obtained by joint minimization. This can be done efficiently by Monte Carlo methods, for example by FFT-based annealing using a Markov chain that alternates between (global) transitions from one array to the other. Experiments are reported in optical astronomy, with space telescope data, and computed tomography.