Fuzzy linear programming based radar subset selection for target localization in UAV system

Xiaojing Liu, Wen Hu, Haiyang Zheng
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引用次数: 2

Abstract

This paper represented a method of radar subset selection based on fuzzy linear programming (FLP) for target localization in UAV radar system. Unlike existing radar subset selection policies, the cost factor of each radar is assigned a fixed value, from the fact that it is determined by the will of the decision maker in most cases, thus has a large extent of fuzziness — in this paper, we consider it as a fuzzy variable when selecting a subset of radars to achieve the localization minimum estimation mean-square error (MSE). In the first, the Cramer Rao bound (CRB) for localization estimation error is derived, which is used as a performance metric when selecting a subset of radars needed to complete a task. In the second, a heuristic algorithm based on FLP is proposed for radar subset selection. At last, a fuzzy objective function is constructed, and a reasonable solution is given by using the fuzzy simulation technique comparing with the case, where the cost factors in the objective function are previously assigned.
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基于模糊线性规划的无人机目标定位雷达子集选择
提出了一种基于模糊线性规划(FLP)的无人机雷达目标定位子集选择方法。与现有的雷达子集选择策略不同,每个雷达的成本因子被赋予一个固定的值,因为它在大多数情况下是由决策者的意志决定的,因此具有很大程度的模糊性——在本文中,我们将其作为一个模糊变量来选择雷达子集,以实现定位最小估计均方误差(MSE)。首先,推导了定位估计误差的Cramer - Rao界(CRB),并将其作为选择完成任务所需雷达子集的性能指标。其次,提出了一种基于FLP的雷达子集选择启发式算法。最后,构造了一个模糊目标函数,并利用模糊仿真技术与目标函数中成本因子的赋值情况进行了比较,给出了合理的解。
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