基于目標(biāo)容量的網(wǎng)絡(luò)化雷達(dá)功率分配方案

戴金輝; 嚴(yán)俊坤; 王鵬輝; 劉宏偉

doi:10.11999/JEIT200873

基于目標(biāo)容量的網(wǎng)絡(luò)化雷達(dá)功率分配方案

doi: 10.11999/JEIT200873

西安電子科技大學(xué)雷達(dá)信號處理國家重點(diǎn)實(shí)驗(yàn)室西安 710071

基金項(xiàng)目: 國家自然科學(xué)基金(62071345)，國家杰出青年科學(xué)基金(61525105)，高等學(xué)校學(xué)科創(chuàng)新引智計(jì)劃(111 project, B18039)，陜西省自然科學(xué)基金(2020JQ-297)，中國航空科學(xué)基金(201920081002)，雷達(dá)信號處理國家重點(diǎn)實(shí)驗(yàn)室基金(61424010406)

詳細(xì)信息

作者簡介:
戴金輝：男，1993年生，博士生，研究方向?yàn)榫W(wǎng)絡(luò)化雷達(dá)資源分配、目標(biāo)跟蹤

嚴(yán)俊坤：男，1987年生，副教授，博士生導(dǎo)師，研究方向?yàn)檎J(rèn)知雷達(dá)、目標(biāo)跟蹤與定位、協(xié)同探測等

王鵬輝：男，1984年生，副教授，碩士生導(dǎo)師，研究方向?yàn)槔走_(dá)自動目標(biāo)識別、機(jī)器學(xué)習(xí)與模式識別等

劉宏偉：男，1971年生，教授，博士生導(dǎo)師，研究方向?yàn)榫W(wǎng)絡(luò)化雷達(dá)、寬帶雷達(dá)信號處理、雷達(dá)自動目標(biāo)識別、自適應(yīng)和陣列信號處理及目標(biāo)檢測等

通訊作者:
嚴(yán)俊坤　jkyan@xidian.edu.cn

中圖分類號: TN958
計(jì)量
- 文章訪問數(shù): 894
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2020-10-12
- 修回日期: 2021-01-02
- 網(wǎng)絡(luò)出版日期: 2021-01-07
- 刊出日期: 2021-09-16

Target Capacity Based Power Allocation Scheme in Radar Network

National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China

Funds: The National Natural Science Foundation of China (62071345), The National Science Fund for Distinguished Yong Scholars (61525105), The Fund for Foreign Scholars in University Research and Teaching Programs (111 project, B18039), The Natural Science Foundation of Shaanxi Province (2020JQ-297), The Aeronautical Science Foundation of China (201920081002), The Foundation of National Radar Signal Processing Laboratory (61424010406)

摘要

摘要: 針對現(xiàn)有網(wǎng)絡(luò)化雷達(dá)功率資源利用率低的問題，該文提出一種基于目標(biāo)容量的功率分配(TC-PA)方案以提升保精度跟蹤目標(biāo)個(gè)數(shù)。TC-PA方案首先將網(wǎng)絡(luò)化雷達(dá)功率分配模型制定為非光滑非凸優(yōu)化問題；而后引入Sigmoid函數(shù)將原問題松弛為光滑非凸優(yōu)化問題；最后運(yùn)用近端非精確增廣拉格朗日乘子法(PI-ALMM)對松弛后的非凸問題進(jìn)行求解。仿真結(jié)果表明，PI-ALMM對于求解線性約束非凸優(yōu)化問題可以較快地收斂到一個(gè)穩(wěn)態(tài)點(diǎn)。另外，相比傳統(tǒng)功率均分方法和遺傳算法，所提TC-PA方案可以最大限度地提升目標(biāo)容量。
- 網(wǎng)絡(luò)化雷達(dá) /
- 多目標(biāo)跟蹤 /
- 資源分配 /
- 非凸優(yōu)化
Abstract: In view of the fact that low power resource utilization rate exists in radar network, a Target Capacity based Power Allocation (TC-PA) scheme is proposed to increase the number of the targets that satisfy tracking accuracy requirements. Firstly, this scheme formulates the power allocation model of radar network as a non-smooth and non-convex optimization problem. Then the original problem is relaxed into a smooth and non-convex problem through introducing Sigmoid function. Finally, the relaxed non-convex problem is solved by utilizing the Proximal Inexact Augmented Lagrangian Multiplier Method (PI-ALMM). Simulation results show that the PI-ALMM can quickly converge to a stationary point for solving the non-convex optimization problem with linear constraints. Moreover, the proposed TC-PA scheme outperforms the traditional uniform power allocation method and genetic algorithm, in terms of target capacity.
- Radar network /
- Multiple target tracking /
- Resource allocation /
- Non-convex optimization

HTML全文

圖 1 節(jié)點(diǎn)和目標(biāo)的空間位置分布

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圖 2 多目標(biāo)跟蹤精度閾值

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圖 3 不同方法的目標(biāo)容量

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圖 4 均勻分配和PI-ALMM滿足跟蹤精度的目標(biāo)下標(biāo)

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圖 5 采用PI-ALMM各節(jié)點(diǎn)功率分配結(jié)果

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圖 6 目標(biāo)函數(shù)值與迭代次數(shù)的關(guān)系

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圖 7 $\phi _k^j$與迭代次數(shù)的關(guān)系

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表 1 PI-ALMM求解流程

　(1) 初始化參數(shù)$\rho > 0$,$\alpha > 0$, $0 < c \le {1 / {\bar L}}$, $\ell > - \tau $,

　$0 < \beta \le 1$，及迭代下標(biāo)$j = 0$；

　(2) 初始化變量${\boldsymbol{p}}_{q,k}^j{\rm{ = }}{\left( {{{{{p}}_{{\rm{total}}}^1} / {Q{{,{{p}}_{{\rm{total}}}^2} / Q}{{, ··· ,{{p}}_{{\rm{total}}}^N} / Q}}}} \right)^{\rm{T}}}$,

　令${\boldsymbol{p}}_k^j = \left( {{\boldsymbol{p}}_{1,k}^j;{\boldsymbol{p}}_{2,k}^j; ··· ;{\boldsymbol{p}}_{Q,k}^j} \right)$, ${\boldsymbol}_k^j{\rm{ = }}{\boldsymbol{p}}_k^j$及${\boldsymbol{a} }_k^j{\rm{ = } }{ {{{\textit{0}}} }_{N \times 1} }$；

　(3) 計(jì)算$L\left( {{{\boldsymbol{p}}_k},{{\boldsymbol}_k};{{\boldsymbol{a}}_k}} \right)$關(guān)于${{\boldsymbol{p}}_k}$的梯度

　$\begin{array}{l} { {\text{?} }_{ { {\boldsymbol{p} }_k} } }L\left( { { {\boldsymbol{p} }_k},{ {\boldsymbol }_k};{ {\boldsymbol{a} }_k} } \right) = { {\nabla }_{ { {\boldsymbol{p} }_k} } }f\left( { { {\boldsymbol{p} }_k} } \right) + { {\boldsymbol{A} }^{\rm{T} } }{ {\boldsymbol{a} }_k} + \rho { {\boldsymbol{A} }^{\rm{T} } } \\ \begin{array}{*{20}{c} } {}&{}&{} \end{array}\left( { {\boldsymbol{A} }{ {\boldsymbol{p} }_k} - { {\boldsymbol{p} }_{ {\rm{total} } } } } \right) + \ell \left( { { {\boldsymbol{p} }_k} - { {\boldsymbol }_k} } \right) \end{array}d{array}$；

　(4) 循環(huán)

　　(a) ${\boldsymbol{a}}_k^{j + 1} = {\boldsymbol{a}}_k^j + \alpha \left( {A{\boldsymbol{p}}_k^j - {{\boldsymbol{p}}_{{\rm{total}}}}} \right)$；

　　(b) ${\boldsymbol{p} }_k^{j + 1} = {\left[ { {\boldsymbol{p} }_k^j - c \cdot { \nabla_{ {\boldsymbol{p} }_k^j} }L\left( { {\boldsymbol{p} }_k^j,{\boldsymbol }_k^j;{\boldsymbol{a} }_k^{j + 1} } \right)} \right]_ + }$；

　　(c) ${\boldsymbol}_k^{j + 1} = {\boldsymbol}_k^j + \beta \left( {{\boldsymbol{p}}_k^{j + 1} - {\boldsymbol}_k^j} \right)$；

　　(d) $j = j + 1$；

　(5) 直到$\left| {f\left( {{\boldsymbol{p}}_k^j} \right) - f\left( {{\boldsymbol{p}}_k^{j - 1}} \right)} \right| \le \varepsilon $($\varepsilon $為給定算法終止門限)，退
　　出循環(huán)，令功率分配結(jié)果${\boldsymbol{p}}_k^{{\rm{opt}}} = {\boldsymbol{p}}_k^j$。

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