Optimization In Resource-limited Wireless Localization Networks

With the development of urbanization,location based service(LBS)is becoming ubiquitous nowadays.Emerging technical services such as smart medical care,unmanned driving,indoor environment monitoring,etc.,all require high-precision positioning technology.Now the most widely used Global Navigation Satellite Systems(GNSS)is difficult to guarantee the positioning accuracy of indoor and urban areas.The importance of wireless positioning network as a supplement to seamless positioning system is increasing.In view of the differences in anchor positions and the limited resources carried by nodes,allocating limited resources to more "useful" nodes is a crucial idea to improve the positioning accuracy,as well as the energy efficiency of wireless localization networks.Recent investigations show that proper network scheduling strategies can significantly enhance the system performance.In addition to the current efficient non-data aided strategies,we find that some silent nodes,called "eavesdroppers",can be of helpful without transmitting any signals.We first formulate the eavesdropping scheduling policy in practical asynchronous cooperative wireless localization networks.The ranging signal is formulated based on the scheduling strategy of frequency division multiplexing without auxiliary information.The Cramer Rao Lower Bound(CRLB)of the positioning accuracy is derived and then we perform resource optimization in different eavesdropping based strategies.Most existing investigations are carried out based on the Cramer Rao Lower Bound(CRLB),which is not always achievable,especially in low SNR regimes.In this paper,we mainly focus on the mean square error(MSE)achieved directly from various localization algorithms.Due to the fact that,MSE can not be handled in a closed form,learning based frameworks are thus provided.Aiming at the exponential increased state space in the multi-agent-scenario,low complexity alternating solutions are provided.In addition,a robust scheme is given considering the measurement error,which provide the solution for ranging links with clock deviation or obstruction.It is verified by simulations and experiment that,optimized allocation of resources in a resource-limited environment can improve the performance of the positioning network,and the numerical results verify the advantages of eavesdropping under resource-limited conditions.MSE-based resource allocation framework can decrease the error caused by ideal conditions.The accuracy of distributed resource optimization strategy is close to the CRLB,and is significantly better than the uniform allocation strategy.Framework proposed in this paper can provide high-precision link selection schemes in actual positioning scenarios,further verifying the effectiveness of this framework.