A robust TDOA based solution for source location using mixed Huber loss

This paper proposes a source localization solution robust to measurement outliers in time differences of arrivals(TDOA)measurements.The solution uses a piecewise loss function named as mixed Huber loss(MHL)proposed based on the classical Huber loss(HL)and its refined version.The MHL is able to effectively mitigate the impact of all levels of measurement outliers by setting two triggering thresholds.In practice,appropriate triggering threshold values can be obtained through simulation given the level of measurement noise and a rough range of potential measurement outliers.A clustering based approach is proposed to further improve the robustness of localization solution against reference sensor related outliers.Simulations are included to examine the solution's performance and compare it with several benchmarks.The proposed MHL based solution is shown to be superior to the classical solution and the benchmarks.The solution is shown to be even robust to multiple measurement outliers.Furthermore,the influence of range measurement outliers in the reference sensor can be effectively mitigated by the clustering based approach.

GA-BASED PID NEURAL NETWORK CONTROL FOR MAGNETIC BEARING SYSTEMS

In order to overcome the system non-linearity and uncertainty inherent in magnetic bearing systems, a GA（genetic algnrithm）-based PID neural network controller is designed and trained tO emulate the operation of a complete system （magnetic bearing, controller, and power amplifiers）. The feasibility of using a neural network to control nonlinear magnetic bearing systems with unknown dynamics is demonstrated. The key concept of the control scheme is to use GA to evaluate the candidate solutions （chromosomes）, increase the generalization ability of PID neural network and avoid suffering from the local minima problem in network learning due to the use of gradient descent learning method. The simulation results show that the proposed architecture provides well robust performance and better reinforcement learning capability in controlling magnetic bearing systems.

Iterative-Reweighting-Based Robust Iterative-Closest-Point Method

In point cloud registration applications,noise and poor initial conditions lead to many false matches.False matches significantly degrade registration accuracy and speed.A penalty function is adopted in many robust point-to-point registration methods to suppress the influence of false matches.However,after applying a penalty function,problems cannot be solved in their analytical forms based on the introduction of nonlinearity.Therefore,most existing methods adopt the descending method.In this paper,a novel iterative-reweighting-based method is proposed to overcome the limitations of existing methods.The proposed method iteratively solves the eigenvectors of a four-dimensional matrix,whereas the calculation of the descending method relies on solving an eight-dimensional matrix.Therefore,the proposed method can achieve increased computational efficiency.The proposed method was validated on simulated noise corruption data,and the results reveal that it obtains higher efficiency and precision than existing methods,particularly under very noisy conditions.Experimental results for the KITTI dataset demonstrate that the proposed method can be used in real-time localization processes with high accuracy and good efficiency.

Terrain based co-operative UAV mapping of complex obstacles using 2-D splinegon

Purpose-This paper aims to describe a recently proposed algorithm in terrain-based cooperative UAV mapping of the unknown complex obstacle in a stationary environment where the complex obstacles are represented as curved in nature.It also aims to use an extended Kalman filter(EKF)to estimate the fused position of the UAVs and to apply the 2-D splinegon technique to build the map of the complex shaped obstacles.The path of the UAVs are dictated by the Dubins path planning algorithm.The focus is to achieve a guaranteed performance of sensor based mapping of the uncertain environments using multiple UAVs.Design/methodology/approach–An extended Kalman filter is used to estimate the position of the UAVs,and the 2-D splinegon technique is used to build the map of the complex obstacle where the path of the UAVs are dictated by the Dubins path planning algorithm.Findings-The guaranteed performance is quantified by explicit bounds of the position estimate of the multiple UAVs for mapping of the complex obstacles using 2-D splinegon technique.This is a newly proposed algorithm,the most efficient and a robust way in terrain based mapping of the complex obstacles.The proposed method can provide mathematically provable and performance guarantees that are achievable in practice.Originality/value-The paper describes the main contribution in mapping the complex shaped curvilinear objects using the 2-D splinegon technique.This is a new approach where the fused EKF estimated positions are used with the limited number of sensors’measurements in building the map of the complex obstacles.