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首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >Multiple model adaptive estimation with filter spawning
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Multiple model adaptive estimation with filter spawning

机译：滤波器产卵多模型自适应估计

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Multiple model adaptive estimation (MMAE) with filter spawning is used to detect and estimate partial actuator failures on the VISTA F-16. The truth model is a full six-degree-of-freedom simulation provided by Calspan and General Dynamics. The design models are chosen as 13-state linearized models, including first order actuator models. Actuator failures are incorporated into the truth model and design model assuming a "failure to free stream." Filter spawning is used to include additional filters with partial actuator failure hypotheses into the MMAE bank. The spawned filters are based on varying degrees of partial failures (in terms of effectiveness) associated with the complete-actuaton-failure hypothesis with the highest conditional probability of correctness at the current time. Thus, a blended estimate of the failure effectiveness is found using the filters' estimates based upon a no-failure hypothesis, a complete actuator failure hypothesis, and the spawned filters' partial-failure hypotheses. This yields substantial precision in effectiveness estimation, compared with what is possible without spawning additional filters, making partial failure adaptation a viable methodology.
机译：具有过滤器产卵的多种模型自适应估计（MMAE）用于检测和估计Vista F-16上的部分致动器故障。真相模型是CALSPAN和一般动态提供的完整六维自由度模拟。设计型号被选为13状态线性化型号，包括一阶执行器型号。执行器故障被纳入真相模型和设计模型，假设“无法自由流”。过滤器产卵用于包括具有部分执行器故障假设的附加滤波器进入MMAE库。产生的滤波器基于与完整的actuaton故障假设相关的不同程度的部分故障（在有效性方面），其当前时间的正确性概率最高。因此，基于无故障假设，完整的执行器故障假设和生成的滤波器的部分故障假设，使用滤波器的估计来发现失败效率的混合估计。这在有效估计中产生了大量的精度，而无需产出额外过滤器，使部分故障适应是可行的方法。

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