基于多維測(cè)量信息的壓縮感知多目標(biāo)無(wú)源被動(dòng)定位算法
doi: 10.11999/JEIT180333
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1.
陸軍工程大學(xué)通信工程學(xué)院 ??南京 ??210007
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2.
武警部隊(duì) ??北京 ??100089
Compressive Sensing Based Multi-target Device-free Passive Localization Algorithm Using Multidimensional Measurement Information
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1.
College of Communications Engineering, Army Engineering University of PLA, Nanjing 210007, China
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2.
The Chinese Armed Police Force, Beijing 100089, China
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摘要:
無(wú)源被動(dòng)定位是入侵者檢測(cè)、環(huán)境監(jiān)測(cè)以及智能交通等應(yīng)用的關(guān)鍵問(wèn)題之一?，F(xiàn)有的無(wú)源被動(dòng)定位方法可通過(guò)信道狀態(tài)信息獲取多個(gè)維度上的測(cè)量信息，但是現(xiàn)有方案未能充分挖掘多個(gè)信道上的頻率分集以提高定位性能。該文提出一種基于多維測(cè)量信息的壓縮感知多目標(biāo)無(wú)源被動(dòng)定位算法，在壓縮感知框架下利用多維測(cè)量信息的頻率分集提高定位精度和魯棒性。根據(jù)鞍面模型建立無(wú)源字典，將多目標(biāo)無(wú)源被動(dòng)定位問(wèn)題建模成多測(cè)量向量聯(lián)合稀疏恢復(fù)問(wèn)題，并利用多維稀疏貝葉斯學(xué)習(xí)算法估計(jì)目標(biāo)位置向量。仿真結(jié)果表明，該算法能有效利用多維測(cè)量信息提高定位性能。
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關(guān)鍵詞:
- 無(wú)源被動(dòng)定位 /
- 壓縮感知 /
- 多測(cè)量向量 /
- 稀疏貝葉斯學(xué)習(xí)
Abstract:Device-free passive localization is a key issue of the intruder detection, environmental monitoring, and intelligent transportation. The existing device-free passive localization method can obtain the multidimensional measurement information by channel state information, but the existing scheme can not fully exploit the frequency diversity on multiple channels to improve the localization performance. This paper proposes a Compressive Sensing (CS) based multi-target device-free passive localization algorithm using multidimensional measurement information. It takes advantage of the frequency diversity of multidimensional measurement information to improve the accuracy and robustness of localization results under the CS framework. The dictionary is built according to the saddle surface model, and the multi-target device-free passive localization problem is modeled as a joint sparse recovery problem based on multiple measurement vectors. The target location vector is estimated based on the multiple sparse Bayesian learning algorithm. Simulation results indicate that the proposed algorithm can make full use of the multidimensional measurement information to improve the localization performance.
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表 1 聯(lián)合稀疏恢復(fù)算法
(1) 令${\gamma _{{\rm{th}}}} = {10^{ - 3}}$, ${\tau _{\max }} = {10^3}$, ${\eta _{{\rm{th}}}} = - 10\ {\rm{dB}}$, $\gamma = \tau = 0$。 (2) while ($\gamma \ge {\gamma _{{\rm{th}}}}$或$\tau \le {\tau _{\max }}$) do (3) 根據(jù)式(16)和式(17)，計(jì)算${{Σ}}$和${{Π}}$。 (4) 根據(jù)式(19)和式(20)，更新參數(shù)${\alpha _n}$和${\sigma ^2}$。 (5) 令$\gamma \leftarrow \parallel{Y} - {{Φ}}{{Π}}\parallel $, $\tau \leftarrow \tau + 1$。 (6) end while (7) 選擇使$\left\| {{{{y}}^f} - {{Φ}}{{{Π}} _{ \cdot f}}} \right\|$取得最小值的子信道$\hat f$。 (8) $\forall n \in \left\{ {1,2,·\!·\!·,N} \right\}$，若$20\lg ({{{Π}} _{nf}}/\mathop {\max }\limits_i |{{{Π}} _{i\hat f}}|) < {\eta _{{\rm{th}}}}$，則${{{Π}} _{n\hat f}} = 0$。 (9) 令恢復(fù)的位置向量$\hat {{θ}} = {{{Π}} _{ \cdot \hat f}}$，目標(biāo)個(gè)數(shù)${\widehat K} = |\hat {{θ}}|$。 
下載: 導(dǎo)出CSV
表 2 平均定位誤差與定位均方根誤差的比較
定位算法 OMP BP GMP BCS VEM 自有算法($F = 5$) 自有算法($F = 10$) 自有算法($F = 20$) 平均定位誤差 3.2028 1.3028 2.1795 0.8955 0.6196 0.4631 0.2744 0.2584 定位均方根誤差 3.3801 1.5735 2.4543 1.5838 1.0036 0.8392 0.6720 0.4738
下載: 導(dǎo)出CSV
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