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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Genetic Algorithm-Based Sensor Allocation With Nonlinear Centralized Fusion Observable Degree
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Genetic Algorithm-Based Sensor Allocation With Nonlinear Centralized Fusion Observable Degree

机译：非线性集中融合可观度的基于遗传算法的传感器分配

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摘要

As the main performance self-evaluation index of the Kalman filter, the estimation error covariance (EEC) has been used to design the allocation cost function of task and resources for sensor tracking networks. For nonlinear systems, the sensor allocation method based on the EEC needs to adjust the allocation plans after obtaining the filtering results. Meanwhile, recent investigations have indicated that the self-evaluation function EEC of the Kalman filtering is universally inapplicable in practical applications, for which the estimation models are generally mismatched due to difficulty in accurately training parameters and approximation of nonlinear systems. Thereby, the sensors cannot be properly allocated by using the EEC as a preliminary criterion. Alternatively, observable degree (OD) is a naturally quantitative measure on observability and can be utilized to effectively measure the estimation performance. In this paper, the OD analysis with scale transform invariance for nonlinear systems is studied by using the unscented Kalman filter, the pseudostate transition matrix, and the pseudo observation matrix on the basis of the results of linear systems. Afterward, the OD of nonlinear fusion systems, the sensor utilization efficiency, the priority of tasks, and the sensor performance and sensitivity are jointly considered to formulate the optimization problem for sensor allocation. The genetic algorithm with intelligent learning function is employed to solve the optimization problem. Moreover, extensive simulation demonstrates the feasibility of the proposed approach.
机译：作为卡尔曼滤波器的主要性能自我评估指标，估计误差协方差（EEC）已用于设计传感器跟踪网络的任务和资源的分配成本函数。对于非线性系统，基于EEC的传感器分配方法需要在获得滤波结果后调整分配方案。同时，最近的研究表明，卡尔曼滤波的自评估函数EEC在实际应用中普遍不适用，由于难以精确训练参数和逼近非线性系统，估计模型通常不匹配。因此，不能通过使用EEC作为初步标准来正确分配传感器。或者，可观察度（OD）是对可观察性的自然定量度量，可用于有效地测量估计性能。本文基于线性系统的结果，使用无味卡尔曼滤波器，伪状态转换矩阵和伪观测矩阵，研究了非线性系统具有尺度变换不变性的OD分析。然后，结合非线性融合系统的OD，传感器利用效率，任务优先级，传感器性能和灵敏度，共同提出传感器分配的优化问题。采用具有智能学习功能的遗传算法来解决优化问题。此外，大量的仿真证明了该方法的可行性。

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