As the input signal must be informative enough, so that the resulting dataset is enough informative to excite the identification experiment for multi UAVs formation anomaly detection. Based on our previous work on multi UAVs formation anomaly detection, the optimal input signals are designed for two identification strategies, i.e. least squares estimation and improved sparse estimation. Using the variance of the asymptotic distribution corresponding to the unknown parameters, the common trace operation is chosen to construct one numerical optimization problem, whose solution is corresponded to the optimal power spectral. After giving the detailed minimization process, we see that the power spectral corresponding to the optimal input signal is a constant. In addition, for the sake of completeness, one dynamic programming technique in multi UAVs formation anomaly detection is added to complete our early research. Finally, one numerical example illustrates the effectiveness of our proposed theories.
|Number of pages||9|
|Publication status||Published - Aug 2019|
Bibliographical noteFunding Information:
This work is partially supported by the Grants from the National Science Foundation of China (No. 61364014 ) and (No. jxxjb18020 ).
This work is partially supported by the Grants from the National Science Foundation of China (No. 61364014) and (No. jxxjb18020). The author declares that there is no conflict of interests regarding the publication of this paper.
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Copyright 2019 Elsevier B.V., All rights reserved.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Computer Science Applications
- Electrical and Electronic Engineering
- Applied Mathematics