DOA estimation of incoherently distributed sources using importance sampling maximum likelihood

In this paper, an importance sampling maximum likelihood(ISML) estimator for direction-of-arrival(DOA) of incoherently distributed(ID) sources is proposed. Starting from the maximum likelihood estimation description of the uniform linear array(ULA), a decoupled concentrated likelihood function(CLF) is presented. A new objective function based on CLF which can obtain a closed-form solution of global maximum is constructed according to Pincus theorem. To obtain the optimal value of the objective function which is a complex high-dimensional integral,we propose an importance sampling approach based on Monte Carlo random calculation. Next, an importance function is derived, which can simplify the problem of generating random vector from a high-dimensional probability density function(PDF) to generate random variable from a one-dimensional PDF. Compared with the existing maximum likelihood(ML) algorithms for DOA estimation of ID sources, the proposed algorithm does not require initial estimates, and its performance is closer to CramerRao lower bound(CRLB). The proposed algorithm performs better than the existing methods when the interval between sources to be estimated is small and in low signal to noise ratio(SNR)scenarios.

Towards delivery of situational information in distributed environments

In the distributed environment,robots should be able to provide users with adaptive services automatically according to the situational information changing dynamically which is obtained from both users and their environments.The workflow depends on situational information obtained from physical environments and provides context-aware services automatically based on the information retrieved.And the workflow in the business processes and the distributed computing environments have supported the automation of services by connecting tasks.The workflow needs to specify ubiquitous situational information as state-transition constraints.However,the delivery and use of sensor data in the workflow is a difficult problem for the robot system.In order to bridge the gap between applications and low-level constructs and to acquire raw situational information for the execution of the context-aware workflow in the robot systems,this paper presents an approach which can achieve the sensor data transmission between a sensing server and a robot system easily.