Research On Cooperative Localization Method For Multiple Mobile Nodes Under Incomplete Information Condition

Under complex task scenarios with incomplete information conditions,multi-mobile nodes cooperative location has many special advantages including no extra hardware and high-to-perceive identifier.Besides,location information is shared between team members,in order to strengthen the positioning effect of itself and the rest of the team.Moreover,positioning accuracy has been improved.In this paper,the key research on higher error problems based on previous research about multi-mobile node co-location.In the one hand,aiming at multi-mobile node wheel encoders have low self-positioning accuracy;in another hand,aiming at multi-mobile node standard particle filter algorithm has low positioning accuracy.There are two kinds of multi-mobile node co-location methods respectively to solve the above problems,and the main research desks are as follows:At first,multi-mobile node localization is modeled in this paper.Establish multi-mobile node models on two-dimensional land planes,including establishing mobile node movement models and observation models.In addition,combating the theoretical knowledge of mobile node positioning,and standardize accuracy evaluation criteria and conformity assessment methodology of multi-mobile node co-location.At second,aiming to the multi-mobile node localization methods using wheel encoders,there is a problem of cumulative error and coordinate inaccuracy in localization estimation.Thus,there is a multi-mobile node co-location method is proposed under extended Kalman filter framework.According to the fusion of multi-mobile node teams' location information,this method improves the mutual covariance terms,and continuously predicts and corrects the mobile node location error.Besides,adjusting the uncertainty of the covariance by introducing the consistency factor,and effectively solve the problem of low positioning accuracy and inconsistent estimation.The research results show that the method proposed in this paper has smaller positioning error and higher consistent positioning accuracy rather than multi-mobile node self-localization methods.At last,aiming at the problem of low co-location accuracy of multi-mobile nodes in strong nonlinear systems,there is a fusion resampled lossless particle filter multi-mobile node co-location method(UCPF)is proposed under the particle filter framework in this paper.The method can use the lossless importance sampling strategy in the importance sampling stage to perform the lossless transformation on the particles and improve the sampling accuracy,and use the observation information on the remaining mobile nodes to correct the weight of the particles,then using the fusion resampling strategy to solve the problem of loss of particle diversity.The research results show that this method has higher positioning accuracy,rather than the multi-mobile node co-location method via the standard particle filter algorithm.