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一種基于半定松弛技術(shù)的TDOA-FDOA無源定位算法

孫霆,  董春曦,  毛昱

孫霆, 董春曦, 毛昱. 一種基于半定松弛技術(shù)的TDOA-FDOA無源定位算法[J]. 電子與信息學(xué)報(bào), 2020, 42(7): 1599-1605. doi: 10.11999/JEIT190435
引用本文: 孫霆, 董春曦, 毛昱. 一種基于半定松弛技術(shù)的TDOA-FDOA無源定位算法[J]. 電子與信息學(xué)報(bào), 2020, 42(7): 1599-1605. doi: 10.11999/JEIT190435
Ting SUN, Chunxi DONG, Yu MAO. A TDOA-FDOA Passive Positioning Algorithm Based on the Semi-Definite Relaxation Technique[J]. Journal of Electronics & Information Technology, 2020, 42(7): 1599-1605. doi: 10.11999/JEIT190435
Citation: Ting SUN, Chunxi DONG, Yu MAO. A TDOA-FDOA Passive Positioning Algorithm Based on the Semi-Definite Relaxation Technique[J]. Journal of Electronics & Information Technology, 2020, 42(7): 1599-1605. doi: 10.11999/JEIT190435

一種基于半定松弛技術(shù)的TDOA-FDOA無源定位算法

doi: 10.11999/JEIT190435 cstr: 32379.14.JEIT190435

  • 西安電子科技大學(xué)電子工程學(xué)院 西安 710071

詳細(xì)信息
    作者簡介:

    孫霆：男，1994年生，博士生，研究方向?yàn)闊o源定位

    董春曦：男，1970年生，教授，博士生導(dǎo)師，研究方向?yàn)槔走_(dá)/通信輻射源無源定位技術(shù)、高分辨雷達(dá)干擾技術(shù)、雷達(dá)/雷達(dá)對抗系統(tǒng)仿真等

    毛昱：男，1994年生，博士生，研究方向?yàn)檩椛湓醋R(shí)別

    通訊作者:

    董春曦　chxdong@mail.xidian.edu.cn

  • 中圖分類號(hào): TN97

A TDOA-FDOA Passive Positioning Algorithm Based on the Semi-Definite Relaxation Technique

  • School of Electronic Engineering, Xidian University, Xi’an 710071, China

  • 摘要:

    在運(yùn)動(dòng)目標(biāo)的無源定位場景下，閉式算法在低噪聲情況下可以到達(dá)克拉美羅下界(CRLB)，但是這些算法往往不能適應(yīng)較大的測量噪聲環(huán)境。針對目前閉式算法適應(yīng)大噪聲能力較差這一問題，該文聯(lián)合到達(dá)時(shí)間差(TDOA)以及到達(dá)頻率差(FDOA)，提出一種基于半定松弛(SDR)技術(shù)的無源定位算法。該算法首先構(gòu)建傳統(tǒng)閉式解的偽線性方程，其次利用隨機(jī)魯棒最小二乘(SRLS)的思想以及目標(biāo)參數(shù)與額外變量之間的非線性關(guān)系，將無源定位問題轉(zhuǎn)化為了具有2次等式約束的最小二乘問題；隨后，將半定松弛技術(shù)應(yīng)用到這一問題上，約束最小二乘問題松弛為半定規(guī)劃(SDP)問題，最后，借助優(yōu)化工具箱可以有效地對目標(biāo)參數(shù)進(jìn)行求解。該文所提出的算法不需要初始值先驗(yàn)條件，仿真實(shí)驗(yàn)表明了所提算法的有效性。

    Abstract:

    In the passive location of moving target, the closed-form solution can reach Cramér-Rao Lower Bound (CRLB) under the low noise level, but these algorithms often can not adapt to the large measurement noise condition. For this problem, this paper proposes a passive positioning algorithm based on the Semi-Definite Relaxation (SDR) using Time Difference Of Arrival (TDOA) and Frequency Difference Of Arrival (FDOA). Firstly, this method constructs the pseudo-linear equation of the typical closed-form solution. Secondly, the idea of Stochastic Robust Least Squares (SRLS) and the nonlinear relationship between the target parameters and the additional variables are used to transform the localization problem into the least squares problem with quadratic equality. Using Semi-Definite Programming (SDP) technique, constrained least squares problem is then converted into the SDP problem, which is finally solved by the optimization toolbox. The proposed method does not require an initial priori information and simulations show the effectiveness of the proposed method.

    • HTML全文

  • 圖  1  近場目標(biāo)估計(jì)均方根誤差對比

    圖  2  遠(yuǎn)場目標(biāo)估計(jì)均方根誤差對比

    圖  3  不同時(shí)刻目標(biāo)估計(jì)均方根誤差對比

    表  1  觀測站的位置與速度

    序號(hào)位置(m)速度(m/s)
    130010015030–2020
    2400150100–301020
    330050020010–2010
    4350200100102030
    5–100–100–100–202020
    下載: 導(dǎo)出CSV

    表  2  不同算法平均CPU運(yùn)行時(shí)間(s)

    算法平均CPU運(yùn)行時(shí)間(s)
    改進(jìn)TSWLS[16]0.0016
    改進(jìn)的非完全約束算法[17]0.0080
    SDR算法[18]0.1720
    本文方法0.1600
    下載: 導(dǎo)出CSV
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出版歷程
  • 收稿日期:  2019-06-13
  • 修回日期:  2019-10-28
  • 網(wǎng)絡(luò)出版日期:  2020-02-04
  • 刊出日期:  2020-07-23

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