基于目标容量的网络化雷达功率分配方案

戴金辉; 严俊坤; 王鹏辉; 刘宏伟

doi:10.11999/JEIT200873

基于目标容量的网络化雷达功率分配方案

doi: 10.11999/JEIT200873

西安电子科技大学雷达信号处理国家重点实验室西安 710071

基金项目: 国家自然科学基金(62071345)，国家杰出青年科学基金(61525105)，高等学校学科创新引智计划(111 project, B18039)，陕西省自然科学基金(2020JQ-297)，中国航空科学基金(201920081002)，雷达信号处理国家重点实验室基金(61424010406)

详细信息

作者简介:
戴金辉：男，1993年生，博士生，研究方向为网络化雷达资源分配、目标跟踪

严俊坤：男，1987年生，副教授，博士生导师，研究方向为认知雷达、目标跟踪与定位、协同探测等

王鹏辉：男，1984年生，副教授，硕士生导师，研究方向为雷达自动目标识别、机器学习与模式识别等

刘宏伟：男，1971年生，教授，博士生导师，研究方向为网络化雷达、宽带雷达信号处理、雷达自动目标识别、自适应和阵列信号处理及目标检测等

通讯作者:
严俊坤　jkyan@xidian.edu.cn

中图分类号: TN958
计量
- 文章访问数: 902
- HTML全文浏览量: 645
- PDF下载量: 89
- 被引次数: 0
出版历程
- 收稿日期: 2020-10-12
- 修回日期: 2021-01-02
- 网络出版日期: 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: 针对现有网络化雷达功率资源利用率低的问题，该文提出一种基于目标容量的功率分配(TC-PA)方案以提升保精度跟踪目标个数。TC-PA方案首先将网络化雷达功率分配模型制定为非光滑非凸优化问题；而后引入Sigmoid函数将原问题松弛为光滑非凸优化问题；最后运用近端非精确增广拉格朗日乘子法(PI-ALMM)对松弛后的非凸问题进行求解。仿真结果表明，PI-ALMM对于求解线性约束非凸优化问题可以较快地收敛到一个稳态点。另外，相比传统功率均分方法和遗传算法，所提TC-PA方案可以最大限度地提升目标容量。
- 网络化雷达 /
- 多目标跟踪 /
- 资源分配 /
- 非凸优化
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

图 1 节点和目标的空间位置分布

下载: 全尺寸图片幻灯片

图 2 多目标跟踪精度阈值

下载: 全尺寸图片幻灯片

图 3 不同方法的目标容量

下载: 全尺寸图片幻灯片

图 4 均匀分配和PI-ALMM满足跟踪精度的目标下标

下载: 全尺寸图片幻灯片

图 5 采用PI-ALMM各节点功率分配结果

下载: 全尺寸图片幻灯片

图 6 目标函数值与迭代次数的关系

下载: 全尺寸图片幻灯片

图 7 $\phi _k^j$ 与迭代次数的关系

下载: 全尺寸图片幻灯片

表 1 PI-ALMM求解流程

　(1) 初始化参数

$\rho > 0$ ,

$\alpha > 0$ ,

$0 < c \le {1 / {\bar L}}$ ,

$\ell > - \tau$ ,

$0 < \beta \le 1$ ，及迭代下标

$j = 0$ ；

　(2) 初始化变量

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

　令

${\boldsymbol{p}}_k^j = \left( {{\boldsymbol{p}}_{1,k}^j;{\boldsymbol{p}}_{2,k}^j; ··· ;{\boldsymbol{p}}_{Q,k}^j} \right)$ ,

${\boldsymbol{b}}_k^j{\rm{ = }}{\boldsymbol{p}}_k^j$ 及

${\boldsymbol{a} }_k^j{\rm{ = } }{ {{{\textit{0}}} }_{N \times 1} }$ ；

　(3) 计算

$L\left( {{{\boldsymbol{p}}_k},{{\boldsymbol{b}}_k};{{\boldsymbol{a}}_k}} \right)$ 关于

${{\boldsymbol{p}}_k}$ 的梯度

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

　(4) 循环

　　(a)

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

　　(b)

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

　　(c)

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

　　(d)

$j = j + 1$ ；

　(5) 直到

$\left| {f\left( {{\boldsymbol{p}}_k^j} \right) - f\left( {{\boldsymbol{p}}_k^{j - 1}} \right)} \right| \le \varepsilon$ (

$\varepsilon$ 为给定算法终止门限)，退
　　出循环，令功率分配结果

${\boldsymbol{p}}_k^{{\rm{opt}}} = {\boldsymbol{p}}_k^j$ 。

下载: 导出CSV

参考文献(23)

[1]	GRECO M S, GINI F, STINCO P, et al. Cognitive radars: On the road to reality: Progress thus far and possibilities for the future[J]. IEEE Signal Processing Magazine, 2018, 35(4): 112–125. doi: 10.1109/MSP.2018.2822847
[2]	王祥丽, 易伟, 孔令讲. 基于多目标跟踪的相控阵雷达波束和驻留时间联合分配方法[J]. 雷达学报, 2017, 6(6): 602–610. doi: 10.12000/JR17045 WANG Xiangli, YI Wei, and KONG Lingjiang. Joint beam selection and dwell time allocation for multi-target tracking in phased array radar system[J]. Journal of Radars, 2017, 6(6): 602–610. doi: 10.12000/JR17045
[3]	严俊坤, 刘宏伟, 戴奉周, 等. 基于非线性机会约束规划的多基雷达系统稳健功率分配算法[J]. 电子与信息学报, 2014, 36(3): 509–515. doi: 10.3724/SP.J.1146.2013.00656 YAN Junkun, LIU Hongwei, DAI Fengzhou, et al. Nonlinear chance constrained programming based robust power allocation algorithm for multistatic radar systems[J]. Journal of Electronics &Information Technology, 2014, 36(3): 509–515. doi: 10.3724/SP.J.1146.2013.00656
[4]	SHI Chenguang, DING Lintao, WANG Fei, et al. Low probability of intercept-based collaborative power and bandwidth allocation strategy for multi-target tracking in distributed radar network system[J]. IEEE Sensors Journal, 2020, 20(12): 6367–6377. doi: 10.1109/JSEN.2020.2977328
[5]	YAN Junkun, DAI Jinhui, PU Wenqiang, et al. Quality of service constrained-resource allocation scheme for multiple target tracking in radar sensor network[J]. IEEE Systems Journal, 2021, 15(1): 771–779. doi: 10.1109/JSYST.2020.2990409
[6]	YAN Junkun, PU Wenqiang, ZHOU Shenghua, et al. Optimal resource allocation for asynchronous multiple targets tracking in heterogeneous radar networks[J]. IEEE Transactions on Signal Processing, 2020, 68: 4055–4068. doi: 10.1109/TSP.2020.3007313
[7]	XIE Mingchi, YI Wei, KONG Lingjiang, et al. Receive-beam resource allocation for multiple target tracking with distributed MIMO radars[J]. IEEE Transactions on Aerospace and Electronic Systems, 2018, 54(5): 2421–2436. doi: 10.1109/TAES.2018.2818579
[8]	XIE Mingchi, YI Wei, KIRUBARAJAN T, et al. Joint node selection and power allocation strategy for multitarget tracking in decentralized radar networks[J]. IEEE Transactions on Signal Processing, 2018, 66(3): 729–743. doi: 10.1109/TSP.2017.2777394
[9]	YI Wei, YUAN Ye, HOSEINNEZHAD H, et al. Resource scheduling for distributed multi-target tracking in netted colocated MIMO radar systems[J]. IEEE Transactions on Signal Processing, 2020, 68: 1602–1617. doi: 10.1109/TSP.2020.2976587
[10]	ZHANG Haowei, LIU Weijian, XIE Junwei, et al. Joint subarray selection and power allocation for cognitive target tracking in large-scale MIMO radar networks[J]. IEEE Systems Journal, 2020, 14(2): 2569–2580. doi: 10.1109/JSYST.2019.2960401
[11]	严俊坤, 纠博, 刘宏伟, 等. 一种针对多目标跟踪的多基雷达系统聚类与功率联合分配算法[J]. 电子与信息学报, 2013, 35(8): 1875–1881. doi: 10.3724/SP.J.1146.2012.01470 YAN Junkun, JIU Bo, LIU Hongwei, et al. Joint cluster and power allocation algorithm for multiple targets tracking in multistatic radar systems[J]. Journal of Electronics &Information Technology, 2013, 35(8): 1875–1881. doi: 10.3724/SP.J.1146.2012.01470
[12]	YAN Junkun, PU Wenqiang, ZHOU Shenghua, et al. Collaborative detection and power allocation framework for target tracking in multiple radar system[J]. Information Fusion, 2020, 55: 173–183. doi: 10.1016/j.inffus.2019.08.010
[13]	秦童, 戴奉周, 刘宏伟, 等. 火控相控阵雷达的时间资源管理算法[J]. 系统工程与电子技术, 2016, 38(3): 545–550. doi: 10.3969/j.issn.1001-506X.2016.03.11 QING Tong, DAI Fengzhou, LIU Hongwei, et al. Time resource management algorithm for the fire control phased-array radar[J]. Systems Engineering and Electronics, 2016, 38(3): 545–550. doi: 10.3969/j.issn.1001-506X.2016.03.11
[14]	YAN Junkun, ZHANG Peng, DAI Jinhui, et al. Target capacity based simultaneous multibeam power allocation scheme for multiple target tracking application[J]. Signal Processing, 2021, 178: 107794. doi: 10.1016/j.sigpro.2020.107794
[15]	GURBUZ A C, MDRAFI R, and CETINER B A. Cognitive radar target detection and tracking with multifunctional reconfigurable antennas[J]. IEEE Aerospace and Electronic Systems Magazine, 2020, 35(6): 64–76. doi: 10.1109/MAES.2020.2990589
[16]	NEITZ O and EIBERT T F. A plane-wave synthesis approach for 3D monostatic RCS prediction from near-field measurements[C]. 2018 15th European Radar Conference, Madrid, Spain, 2018: 99–102. doi: 10.23919/EuRAD.2018.8546622.
[17]	SONG Wanjun and ZHANG Hou. RCS prediction of objects coated by magnetized plasma via scale model with FDTD[J]. IEEE Transactions on Microwave Theory and Techniques, 2017, 65(6): 1939–1945. doi: 10.1109/TMTT.2016.2645567
[18]	TICHAVSKY P, MURAVCHIK C H, and NEHORAI A. Posterior Cramer-Rao bounds for discrete-time nonlinear filtering[J]. IEEE Transactions on Signal Processing, 1998, 46(5): 1386–1396. doi: 10.1109/78.668800
[19]	ILIEV A, KYURKCHIEV N, MARKOV S. On the approximation of the step function by some sigmoid functions[J]. Mathematics and Computers in Simulation, 2017, 133: 223–234. doi: 10.1016/j.matcom.2015.11.005
[20]	GARULLI A and VICINO A. Convex relaxations in circuits, systems, and control[J]. IEEE Circuits and Systems Magazine, 2009, 9(2): 46–56. doi: 10.1109/MCAS.2008.931737
[21]	ZHANG Jiawei and LUO Zhiquan. A proximal alternating direction method of multiplier for linearly constrained nonconvex minimization[J]. SIAM Journal on Optimization, 2020, 30(3): 2272–2302. doi: 10.1137/19M1242276
[22]	ZHANG Jiawei and LUO Zhiquan. A global dual error bound and its application to the analysis of linearly constrained nonconvex optimization[J]. arXiv preprint arXiv: 2006.16440, 2020.
[23]	GREFENSTETTE J J. Optimization of control parameters for genetic algorithms[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1986, 16(1): 122–128. doi: 10.1109/TSMC.1986.289288