Improved Spatial Registration and Target Tracking Method for Sensors on Multiple Missiles

Abstract

Inspired by the problem that the current spatial registration methods are unsuitable for three-dimensional (3-D) sensor on high-dynamic platform, this paper focuses on the estimation for the registration errors of cooperative missiles and motion states of maneuvering target. There are two types of errors being discussed: sensor measurement biases and attitude biases. Firstly, an improved Kalman Filter on Earth-Centered Earth-Fixed (ECEF-KF) coordinate algorithm is proposed to estimate the deviations mentioned above, from which the outcomes are furtherly compensated to the error terms. Secondly, the Pseudo Linear Kalman Filter (PLKF) and the nonlinear scheme the Unscented Kalman Filter (UKF) with modified inputs are employed for target tracking. The convergence of filtering results are monitored by a position-judgement logic, and a low-pass first order filter is selectively introduced before compensation to inhibit the jitter of estimations. In the simulation, the ECEF-KF enhancement is proven to improve the accuracy and robustness of the space alignment, while the conditional-compensation-based PLKF method is demonstrated to be the optimal performance in target tracking.

A Novel Sea Target Tracking Algorithm for Multiple Unmanned Aerial Vehicles Considering Attitude Error in Low-Precision Geodetic Coordinate Environments

“…To verify the feasibility of the proposed algorithm, a simulation experiment and a practical experiment were conducted and compared with the methods in references [24,25,32]. Ref.…”

“…Online estimation estimates systematic error of the sensor at the current moment online through real-time data. The main methods include the joint estimation algorithm [23] and decoupling estimation algorithm [24,25], which requires setting the model of error change in advance and has limited applicability. These methods are subject to external influence, and the estimation accuracy is not high.…”

A Spatial Registration Method for Multi-UAVs Based on a Cooperative Platform in a Geodesic Coordinate Information-Free Environment

“…Shang J et al [11] used an exact great likelihood algorithm to achieve the spatial registration of two station coast radar systems based on noncooperative targets; the scenario posture is more realistic, but this method does not consider the attitude of the moving sensors and is only applicable in the two-dimensional plane. Lu X et al [12] took the measurement error and attitude error of the sensors into account simultaneously. However, they did not consider the coupling of the sensor measurement error and the attitude error.…”

Bayesian Cramér-Rao Lower Bounds for Prediction and Smoothing of Nonlinear TASD Systems

“…Filtering, prediction and smoothing have attracted wide attention in many engineering applications, such as target tracking [1,2], signal processing [3], sensor registration [4], econometrics forecasting [5], localization and navigation [6,7], etc. For filtering, the Kalman filter (KF) [8] is optimal for linear Gaussian systems in the sense of minimum mean squared error (MMSE).…”