• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文学位 >Source localization via time difference of arrival.
【24h】

Source localization via time difference of arrival.

机译：通过到达时间差进行源定位。

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Accurate localization of a signal source, based on the signals collected by a number of receiving sensors deployed in the source surrounding area is a problem of interest in various fields. This dissertation aims at exploring different techniques to improve the localization accuracy of non-cooperative sources, i.e., sources for which the specific transmitted symbols and the time of the transmitted signal are unknown to the receiving sensors. With the localization of non-cooperative sources, time difference of arrival (TDOA) of the signals received at pairs of sensors is typically employed.;A two-stage localization method in multipath environments is proposed. During the first stage, TDOA of the signals received at pairs of sensors is estimated. In the second stage, the actual location is computed from the TDOA estimates. This later stage is referred to as hyperbolic localization and it generally involves a non-convex optimization. For the first stage, a TDOA estimation method that exploits the sparsity of multipath channels is proposed. This is formulated as an l1-regularization problem, where the l1-norm is used as channel sparsity constraint. For the second stage, three methods are proposed to offer high accuracy at different computational costs. The first method takes a semi-definite relaxation (SDR) approach to relax the hyperbolic localization to a convex optimization. The second method follows a linearized formulation of the problem and seeks a biased estimate of improved accuracy. A third method is proposed to exploit the source sparsity. With this, the hyperbolic localization is formulated as an l1- regularization problem, where the l1-norm is used as source sparsity constraint. The proposed methods compare favorably to other existing methods, each of them having its own advantages. The SDR method has the advantage of simplicity and low computational cost. The second method may perform better than the SDR approach in some situations, but at the price of higher computational cost. The l1-regularization may outperform the first two methods, but is sensitive to the choice of a regularization parameter. The proposed two-stage localization approach is shown to deliver higher accuracy and robustness to noise, compared to existing TDOA localization methods.;A single-stage source localization method is explored. The approach is coherent in the sense that, in addition to the TDOA information, it utilizes the relative carrier phases of the received signals among pairs of sensors. A location estimator is constructed based on a maximum likelihood metric. The potential of accuracy improvement by the coherent approach is shown through the Cramer Rao lower bound (CRB). However, the technique has to contend with high peak sidelobes in the localization metric, especially at low signal-to-noise ratio (SNR). Employing a small antenna array at each sensor is shown to lower the sidelobes level in the localization metric.;Finally, the performance of time delay and amplitude estimation from samples of the received signal taken at rates lower than the conventional Nyquist rate is evaluated. To this end, a CRB is developed and its variation with system parameters is analyzed. It is shown that while with noiseless low rate sampling there is no estimation accuracy loss compared to Nyquist sampling, in the presence of additive noise the performance degrades significantly. However, increasing the low sampling rate by a small factor leads to significant performance improvement, especially for time delay estimation.
机译：基于由部署在源周围区域中的多个接收传感器收集的信号来精确定位信号源是各个领域中关注的问题。本文旨在探索不同的技术来提高非合作信号源的定位精度，即接收传感器不知道其特定发射符号和发射信号时间的信号源。对于非合作源的定位，通常采用传感器对接收信号的到达时间差（TDOA）。提出了一种多径环境下的两阶段定位方法。在第一阶段，估计在传感器对处接收到的信号的TDOA。在第二阶段，根据TDOA估算值计算实际位置。此后阶段称为双曲线定位，通常涉及非凸优化。在第一阶段，提出了一种利用多径信道稀疏性的TDOA估计方法。这被公式化为一个l1正则化问题，其中l1范数用作信道稀疏性约束。对于第二阶段，提出了三种方法以不同的计算成本提供高精度。第一种方法采用半定松弛（SDR）方法将双曲线局部松弛为凸优化。第二种方法遵循问题的线性化公式，并寻求提高准确性的有偏估计。提出了第三种方法来利用源稀疏性。这样，双曲局部化被公式化为l1正则化问题，其中l1范数用作源稀疏约束。所提出的方法与其他现有方法相比具有优势，每种方法都有其自身的优势。 SDR方法具有简单和计算成本低的优点。在某些情况下，第二种方法的性能可能比SDR方法更好，但代价是计算成本较高。 l1正则化的性能可能优于前两种方法，但对正则化参数的选择很敏感。与现有的TDOA定位方法相比，所提出的两阶段定位方法具有更高的精度和鲁棒性。从TDOA信息之外，该方法是一致的，它利用了传感器对之间的接收信号的相对载波相位。基于最大似然度量构造位置估计器。通过Cramer Rao下限（CRB）显示了通过相干方法提高准确性的潜力。但是，该技术必须在定位度量中应对高峰值旁瓣，尤其是在低信噪比（SNR）的情况下。示出在每个传感器处使用小的天线阵列来降低定位度量中的旁瓣水平。最后，评估了以低于常规奈奎斯特速率的速率获取的接收信号的采样的时间延迟和幅度估计的性能。为此，开发了一种CRB，并分析了其随系统参数的变化。结果表明，与Nyquist采样相比，无噪声低速率采样不会导致估计精度损失，但是在存在附加噪声的情况下，性能会大大降低。但是，将低采样率提高一小部分会导致性能显着提高，尤其是对于时间延迟估计而言。

著录项

  • 作者

    Comsa, Ciprian Romeo.;

  • 作者单位

    New Jersey Institute of Technology.;

  • 授予单位 New Jersey Institute of Technology.;
  • 学科 Electrical engineering.
  • 学位 Ph.D.
  • 年度 2012
  • 页码 134 p.
  • 总页数 134
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号