This paper investigates the multi-sensor fused localization of moving targets in a wireless sensor network. Each ultra-wide band (UWB) is assigned stability weight according to its survival time prediction. The measurement accuracy each into constraints distribution based on interactive multi-model method, double algorithm that considers and proposed. Based algorithm, information UWB sensor, in...

In this paper, an improved variational adaptive Kalman filter for cooperative localization with inaccurate prior information is proposed, in which the scale matrix of one-step prediction error covariance adaptively estimated by using expectation-maximization algorithm. A novel alternate iteration strategy proposed to reduce computational complexity method. Convergence analysis and theoretical c...

This paper presents a fast and high‐resolution estimation approach using polarization information combined with angle information for multi‐target localization in bistatic multiple‐input multiple‐output (MIMO) radar. The propagator method (PM) is extended to jointly estimate the direction of departure (DOD), the direction of arrival (DOA) and the polarization paramet...

Mismatch problem has been one of important issues of matched field processing for underwater source detection. Experimental use of MFP has shown that robust range and depth localization is difficult to achieve. In many cases this is due to uncertainty in the environmental inputs required by acoustic propagation models. The paper presents that EMD (Empirical mode decomposition) processing underw...

This paper presents an analysis of H∞ Filter(HF) for Robotics Mapping and Localization with unknown noise statistics. HF which is also known as the minimax filter is proposed in this paper to estimate the robot and landmarks location while robot moves through an unknown environment. Some of the conditions are proposed to ensure that the state covariance in HF is converging to a steady state val...

Source localization in ocean acoustics is posed as a machine learning problem in which data-driven methods learn source ranges directly from observed acoustic data. The pressure received by a vertical linear array is preprocessed by constructing a normalized sample covariance matrix and used as the input for three machine learning methods: feed-forward neural networks (FNN), support vector mach...

This paper presents results from the application of Multiway Partial Least Square (MPLS) as a regressor tool in order to estimate the localization of impacts in an aircraft structure. MPLS is a technique that maximizes the covariance between the predictor matrix X and the predicted matrix Y for each component of the space. The structure can be considered as a small scale version of part of a wi...

This paper presents an algorithm for simultaneous localization and mapping for a mobile robot using monocular vision and odometry. The approach uses Variable State Dimension Filtering (VSDF) flamework to combine aspects of extended Kalman filtering (EKF) and nonlinear batch optimization. This paper describes two primary improvements to the VSDF. The first is to use the maximally informative sta...