Maximum likelihood registration for systemic error based on statistical linear regression

Generally,there are non-random systemic errors in target detection with the cooperative multi-sensor system.In order to solve this problem,a maximum likelihood registration algorithm based on statistical linear regression (SLR-MLR) is presented.The registration equation for the multi-sensor system is established first by jointly maximizing the likelihood function of the target state and systemic error,on the basis of which the proposed algorithm utilizes a set of diverse regression points to handle the linearization problem of the nonlinear measurement transformation.The regression equation for the target state with respect to unbiased measurement is constructed through statistical linear regression,and then the first two statistical properties of the projected state can be obtained.Moreover,the algorithm uses the likelihood maximization iteration to seek the solution of the registration equation,thus achieving the joint estimation for the systemic error and target state.Simulation results show that the SLR-MLR can achieve the registration of multiple sensors in each observation dimension,and has a higher accuracy and near computational complexity compared with the classical MLR.