非理想关联下多传感器系统误差的稳健估计

在数据融合系统中,传感器自身系统误差造成其上报融合中心的目标位置状态出现系统性偏差,若得不到有效估计与补偿,融合系统难以实现预期的性能优势。然而,基于目标关联配对关系而构造的超定方程组是系统误差估计的出发点。复杂环境下,受随机噪声、系统误差、虚警、漏报等因素的干扰,数据关联模块的输出结果常常包含错误关联。针对非理想关联下多传感器系统误差的稳健估计问题,该文提出基于最小截平方的系统误差稳健估计方法,并进一步提出剔除异常方程的重加权最小二乘方法。与最小二乘及最小中值平方相比,所提方法在保证估计器稳健性能的前提下,降低了估计结果对随机噪声的敏感程度。仿真实验验证了所提方法的有效性。     

多雷达系统几种误差配准方法的分析与比较

对多雷达系统现有几种主要误差配准方法即实时质量控制法（RealTimeQuality Control)、最小二乘法（Least Squares)、最大似然法（Maximum Likelihood)和广义最小二乘法（Generalized Least Squares)进行理论分析与比较,利用参考文献[1]的数据对上述几种算法的实际应用效果进行比较,为实际系统选择合适配准算法提供一定依据。     

多传感器误差配准研究

多传感器组网信息融合时,需要对组网中各传感器系统误差进行估计和补偿,以消除各传感器系统误差对融合性能的影响。研究了雷达组网系统误差配准模型,并对最小二乘算法 (LS)、广义最小二乘算法(GLS)、递推最小二乘算法(RLS)、修正 EX 算法等误差配准算法进行对比分析,同时给出了扩维配准模型用于解决多传感器组网配准问题,针对实际工程应用中算法收敛情况难以判断的问题,提出了一种误差配准收敛性判定的方法。根据仿真结果对误差配准算法性能进行分析,同时验证了扩维配准和收敛性判定方法的有效性,支撑配准系统应用。     

最小平方法实现多部雷达数据配准

数据配准是多传感器数据融合中的基本问题。本文针对雷达组网系统，提出一种简单，直接的数据配准方法，称为最小平方估计配准法。配准是数据融合的必要步骤，籍以估计和修理雷达的系统误差，从而使数据融合得以有效实现。本方法可使跟踪同一批目标的两部雷达保持观测结果的一致性。     

基于非合作目标的误差配准算法

误差配准是消除传感器系统误差必不可少的过程。针对非合作目标情况下如何估计传感器系统误差的问题,提出了一种基于线性卡尔曼和最小二乘的三维误差配准算法。该算法考虑了地球曲率的影响, 解决了传统的二维算法无法估计俯仰角系统误差的问题。通过构造系统模型, 将传感器系统误差和目标运动情况统一到同一量测方程中, 并结合线性卡尔曼和最小二乘得到系统误差的估计。仿真结果表明, 该方法能有效地估计包括俯仰角误差在内的多种系统误差。     

基于极大似然的联合多传感器配准与融合

传感器配准和多源融合是多传感器多目标跟踪系统中面临的两个重要问题。多传感器融合的精度一定程度上与传感器固有系统误差相关,为提高融合精度,需要进行多传感器配准。在多传感器多目标跟踪场景下,文中根据传感器量测噪声特性,通过公式推导实现了一种基于极大似然的联合多传感器配准与融合算法。该算法可同时在采样时刻间对传感器系统偏差和目标融合位置进行估计,并对传感器系统误差进行时间递推。仿真结果表明,文中算法具有较高的估计精度,可同时解决多传感器的配准与融合问题。     

基于NLS的多机只测角误差配准算法

多机无源融合定位中的误差配准是目前多传感器误差配中的难点之一。当无源传感器获得的观测量存在系统误差却不进行配准时,多机融合定位的效果将受到严重影响。针对这一情况,在多机只测角无源定位问题中提出了一种基于非线性最小二乘（NLS）的误差配准算法。该算法将多机只测角误差配准问题转换为非线性最小二乘估计问题,并采用高斯–牛顿法求解,即先将非线性量测方程线性化并采用加权最小二乘进行估计,然后进行迭代直至收敛到最优估计值。仿真结果表明,与EKF配准算法相比,当观测时间足够长时,本文提出的NLS误差配准算法的定位误差可以接近克拉美罗限（CRLB）,并且对系统误差的估计精度非常高。      

基于地心地固坐标系的多运动平台实时配准方法

固定平台情况下基于地心地固（ECEF）坐标系的配准模型,在运动平台中已不适用,鉴于此,在考虑系统误差固定不变的条件下,推导了基于ECEF坐标系的多运动平台多传感器的配准模型,并提出采用卡尔曼（Kalman）滤波方法来估计系统偏差,实现了对固定系统误差的实时估计,便于工程应用实现。仿真表明了该方法的有效性。     

基于视觉物体识别的抗差岭估计定位算法

基于视觉物体识别的室内定位算法是一种新型的室内定位解决方案,算法通过物体检测、位置匹配、定位方程解算等步骤确定用户位置。然而,受到单目相机视域较小和物体检测算法精度较低的影响,根据检测物体测距信息而构成的定位方程存在严重的病态性,极大降低了算法的定位成功率和定位精度。因此,该文提出一种抗差岭估计定位解算算法,通过引入岭参数将无偏估计变为有偏估计,实现均方误差最小约束条件下的最优位置估计,并利用迭代选权降低了质量较差的观测量对定位精度的影响。实验结果表明,与OLS (Ordinary Least Square), LM (Levenberg-Marquardt)和RR (Ridge Regression)算法相比,该文提出的抗差岭估计定位解算算法能够有效提高基于视觉物体识别的室内定位方法的成功率和精度。     

基于互信息的航迹对准关联算法

针对系统误差对目标航迹的影响,研究了在组网雷达存在系统误差情况下的航迹对准关联问题,并将该影响表示为目标航迹的旋转量和平移量;提出一种基于互信息的系统误差配准前航迹对准关联算法,该算法采用最大互信息理论来估计目标航迹数据的相对旋转量和平移量,将雷达组网中雷达上报的目标航迹数据对准到融合中心,从而避开了估计雷达组网系统误差,实现了误差配准前的航迹对准关联,能够为后端的系统误差配准提供可靠的关联航迹数据。     

Robust antenna array calibration and accurate angle estimation based on least trimmed squares

A robust antenna array calibration and single target angle estimation algorithm is proposed. The proposed algorithm is based on the least trimmed squares algorithm and operates in two steps. First, the conventional least squares algorithm is used to estimate the intermediate phases (or angle) and the residual values at each element are calculated. In the second step, it excludes large residual elements and uses only the smallest of them, which prevents large errors during the angle estimation. The least trimmed-based phase difference approximation algorithm is simple to implement and is a practical way of mitigating errors at the antenna elements that are due to hardware and imperfect calibration. The results demonstrate that our proposed algorithm is robust and outperforms other algorithms in three scenarios.     

Low complexity LTS-based NLOS error mitigation for localization

In this paper, we propose a low complexity and robust non-line-of-sight error mitigation technique for positioning applications. It is based on the least trimmed squares estimation and it can reject BSs with large residuals from measured data while using the small ones for final position calculation. In moderate non-line-of-sight environments, our proposed algorithm achieves the best result while keeping the estimation simple. In heavy non-line-of-sight environments, our proposed algorithm achieves a similar performance to a more complex, more accurate techniques. We show that the least trimmed squares estimation can be used to mitigate non-line-of-sight errors under any channel condition.     

Accurate and robust reconstruction of a surface model of the proximal femur from sparse-point data and a dense-point distribution model for surgical navigation

Constructing a 3-D surface model from sparse-point data is a nontrivial task. Here, we report an accurate and robust approach for reconstructing a surface model of the proximal femur from sparse-point data and a dense-point distribution model (DPDM). The problem is formulated as a three-stage optimal estimation process. The first stage, affine registration, is to iteratively estimate a scale and a rigid transformation between the mean surface model of the DPDM and the sparse input points. The estimation results of the first stage are used to establish point correspondences for the second stage, statistical instantiation, which stably instantiates a surface model from the DPDM using a statistical approach. This surface model is then fed to the third stage, kernel-based deformation, which further refines the surface model. Handling outliers is achieved by consistently employing the least trimmed squares (LTS) approach with a roughly estimated outlier rate in all three stages. If an optimal value of the outlier rate is preferred, we propose a hypothesis testing procedure to automatically estimate it. We present here our validations using four experiments, which include 1) leave-one-out experiment, 2) experiment on evaluating the present approach for handling pathology, 3) experiment on evaluating the present approach for handling outliers, and 4) experiment on reconstructing surface models of seven dry cadaver femurs using clinically relevant data without noise and with noise added. Our validation results demonstrate the robust performance of the present approach in handling outliers, pathology, and noise. An average 95-percentile error of 1.7-2.3 mm was found when the present approach was used to reconstruct surface models of the cadaver femurs from sparse-point data with noise added.     

Robust nonrigid registration to capture brain shift from intraoperative MRI

We present a new algorithm to register 3-D preoperative magnetic resonance (MR) images to intraoperative MR images of the brain which have undergone brain shift. This algorithm relies on a robust estimation of the deformation from a sparse noisy set of measured displacements. We propose a new framework to compute the displacement field in an iterative process, allowing the solution to gradually move from an approximation formulation (minimizing the sum of a regularization term and a data error term) to an interpolation formulation (least square minimization of the data error term). An outlier rejection step is introduced in this gradual registration process using a weighted least trimmed squares approach, aiming at improving the robustness of the algorithm. We use a patient-specific model discretized with the finite element method in order to ensure a realistic mechanical behavior of the brain tissue. To meet the clinical time constraint, we parallelized the slowest step of the algorithm so that we can perform a full 3-D image registration in 35 s (including the image update time) on a heterogeneous cluster of 15 personal computers. The algorithm has been tested on six cases of brain tumor resection, presenting a brain shift of up to 14 mm. The results show a good ability to recover large displacements, and a limited decrease of accuracy near the tumor resection cavity.     

Efficient closed-form solution for target localization using TDOA measurements in the presence of sensor position errors

An efficient solution for locating a target was proposed, which by using time difference of arrival (TDOA) measurements in the presence of random sensor position errors to increase the accuracy of estimation. The cause of position estimation errors in two-stage weighted least squares (TSWLS) method is analyzed to develop a simple and effective method for improving the localization performance. Specifically, the reference sensor is selected again and the coordinate system is rotated according to preliminary estimated target position by using TSWLS method, and the final position estimation of the target is obtained by using weighted least squares (WLS). The proposed approach exhibits a closed-form and is as efficient as TSWLS method. Simulation results show that the proposed approach yields low estimation bias and improved robustness with increasing sensor position errors and thus can easily achieve the Cramer-Rao lower bound (CRLB) easily and effectively improve the localization accuracy.     

基于小生境遗传算法的雷达组网误差配准

多传感器信息融合须进行误差配准。传统的误差配准技术采用RTQC、最小二乘法或极大似然估计法,将非线性方程进行线性化,而线性化过程会引入误差。给出了一种基于小生境遗传算法的误差配准算法,该方法在采用基于ECEF坐标系的误差配准技术的基础上,克服了将非线性方程线性化带来的误差,并在传统遗传算法的基础上引入小生境技术,提高了遗传算法全局寻优能力、收敛速度以及系统误差估计结果的精度。最后,将该方法与基于ECEF坐标系的最小二乘法及传统遗传算法进行了比较,仿真实验结果验证了算法的有效性。     

基于小波分析和鲁棒最小二乘的信息融合估计算法

对于农田土壤的多传感器检测系统来说,其测量的信息具有非线性和时空变异等特性,因此对信息融合方法的基本要求是具有鲁棒性和并行处理能力.应用小波分析理论,对原始测量数据进行了降噪处理,使降噪后的数据更能反映土壤的本质及变化规律:应用鲁棒最小二乘估计技术可以对不同传感器数据进行综合处理,去除冗余,克服歧义,得到比任何单个传感器更全面、更准确、更可靠的信息.针对农业中的自然环境具有很强的不确定性和经验性,运用基于小波分析的降噪和现代信息融合思想,提出了一种基于小波降噪和鲁棒最小二乘的信息融合估计方法.通过实验分析,其结果表明该方法是可行和值得研究的.     

基于加权总体最小二乘正则化方法的混合滤波器组最优化设计

模拟分析滤波器组的实现欠理想、系统噪声以及数字综合滤波器有效阶数实现所带来的系统误差均有可能造成混合滤波器组的设计出现解不稳定、无唯一解等病态问题,影响混合滤波器组的准确重构效果。本文首先给出了满足准确重构条件下,以综合滤波器组频域响应为求解变量的混合滤波器组线性求解模型。针对线性方程中系数矩阵以及目标向量受扰动误差影响特点,提出一种新的基于加权总体最小二乘正则化算法的IIR形式综合滤波器设计方法。算法以系统扰动误差最小化为目标函数,根据随机误差变量的二阶统计特性,采用加权总体最小二乘算法抑制滤波器实现误差以及随机噪声等扰动因素影响,使得到的综合滤波器组频域响应解的加权误差平方和最小化,并通过Tikhonov正则化方法优化病态情况下方程组解的稳定性。提出一种IIR类型的综合滤波器系数的求解算法,并利用正则化方法优化滤波器系数,提高系统稳定性。该方法可应用于过采样混合滤波器组的设计。仿真结果表明该算法的有效提高系统鲁棒性和改善重构性能。      

多传感器配准估计方法与实现

采用地心地固坐标系作为统一坐标系,研究了多传感器组网中的配准估计问题。首先论述了传感器配准的现实意义和传感器偏差的客观来源。随后给出了多传感器配准问题的数学模型描述,并据此给出了配准参数状态矢量。然后利用单目标在不同传感器探测中的量测,在地心地固坐标系下得出偏差方程,采用一阶泰勒展开进行近似,给出各传感器距离、方位角、仰角偏差的线性化公式,并使用最小二乘法估计出各传感器的实时配准参数。最后通过一个Matlab仿真实例,验证了上述方法的有效性。