KEYWORDS: Unmanned aerial vehicles, Power consumption, Matrices, Computer programming, Systems modeling, 3D modeling, Signal to noise ratio, Signal attenuation, Satellite navigation systems, Interference (communication)
For UAV swarms, accurate cooperative localization capability is essential for many applications, especially in GNSSdenied environments where absolute localization information is jammed. The accuracy of cooperative localization is related to the power of the signal transmitted by UAVs. Therefore, for the lifetime and localization accuracy of each UAV, power allocation strategies are crucial issues. The power allocation scheme is looked at in this paper to obtain the optimal resource allocation for UAV swarms. First, in both non-jammed and jammed localization scenarios, the Cramer- Rao lower bound (CRLB) is used as the evaluation index for cooperative localization accuracy. Then, according to the characteristics of the power allocation issue, it is converted into a semidefinite programming (SDP) problem with the limitation of minimum localization accuracy, i.e., the maximum CRLB. The power allocation issues in either scenario can be resolved by SDP. The outcomes of the numerical simulation demonstrate that the proposed optimal scheme can reduce resource consumption while still achieving the needed localization accuracy.
The selection of targets for communication jamming directly affects the establishment of electromagnetic dominance at battlefields and it is the core content for planning combat operations in battlefield communications. Modern warfare is full of high-intensity conflicts and confrontations, and the battlefield information acquired from observation is incomplete and inaccurate sometimes. The existing methods to select targets to attack by communication jammers focus more on pre-war planning, are not closely integrated with the dynamic changes in the combat process, and do not take the cause-effect relationship into full consideration. This paper proposed a method of constructing dynamic Bayesian networks to select targets to attack by communication jammers, provided the algorithm flow of the method, and conducted a simulation analysis with an arithmetic example.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.