)1 School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410083, China; [EMAIL_ADDRESS] (B.G. )1 School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410014, China; [EMAIL_ADDRESS] (B.G. )1 School of Automation, Central South University, Changsha 410083, China; [EMAIL_ADDRESS] (B.G. )1 School of Automation, Central South University, Changsha 410083, China; [EMAIL_ADDRESS] (Y.Z. ); [EMAIL_ADDRESS] (B.G. )1 School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410014, China; [EMAIL_ADDRESS] (B.G. )1 School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410014, China; [EMAIL_ADDRESS] (B.G. )1 School of Automation, Central South University, Changsha 410083, China; [EMAIL_ADDRESS] (B.G. )1 School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410014, China; [EMAIL_ADDRESS] (B.G. )1 School of Electrical and the determinant of FIM or minimizing the trace of error covariance matrix of the estimator [2; 17; 18; 19]. The optimal trajectory of a single mobile sensor is determined by maximizing the determinant of FIM under the condition of adding state constraints on the observer trajectory [20]. When the absolute elevation of the sensor is constant, [PERSON] et al. [21] proposes a new simple sensor optimal deployment criterion based on minimizing the trace of error covariance matrix. Based on the optimization criterion of minimizing the mean square error (MSE), an own-ship path optimization algorithm by using the gradient-descent method in \(xy\) plane and the grid search optimization method in \(z\) axis is presented [22]. A distributed path optimization algorithm is applied when the communication distance constraints and no-fly zone are considered [23]. Most estimation methods generally assume that the variance of measurement noise is known a priori for the purpose of bias compensation. In fact, the noise variance is generally unknown and needs to be approximated in advance. The least-square solution for the target dynamics is calculated and plugged into bearing measurement equations to compute the noise in the first step. Then the \"true\" noise is used to construct the pseudolinear vector and form the final bias-compensation PLE result which is still biased [24]. As the first contribution of this paper, we analyze the properties of Gaussian noise along with the angle measurement equations and newly discover the potential relationship between the determinant of the extended coefficient matrix of the equation and the noise variance. A BC method for target localization on \(xy\) plane and the target position in \(z\)-axis direction based on the pseudo-linear estimation method is proposed thereafter. Optimizing the increment of FIM determinant over a finite time horizon rather than the whole time span makes trajectory planning less effective [25]. As the second contribution of this paper, a sensor trajectory planning algorithm based on greedy strategies maximizes the increment between two consecutive FIM determinants to realize the next location of a single sensor. It is proven that the increment equals the aggregation of FIM determinants constituted by the sensor location at next time step and the combination of all past locations. Considering the spherical constraints of target safety area and sensor movement area, the optimal solution always sits on the area surface. Based on this fact, an analytical solution is suggested to solve the maximization problem. The rest of this paper is organized as follows. Section 2 formulates the problem of target localization in 3D space, including the notations and localization methods. Section 3 proposes a BC estimator and bias compensation weighted instrumental variable (BC-WIV) estimator. In Section 4, the sensor maneuvering algorithm in the composition of two trajectories is designed in detail with the analysis of the FIM increment.