Tracking performance significantly relies on the quality of filter initialization, including accuracy and consistency, especially in a nonlinear filtering system. Most existing methods are built on assumptions that the target position varies linearly with time or the dynamic model is linear. In practical applications where the assumptions are violated, these methods may suffer from performance degradation or cannot be applied. To address this limitation, a general model-based initialization method for both linear and nonlinear dynamic systems is proposed. According to the dynamic model, the relationships between the first several measurements and the state to be initialized are analyzed and several equations are constructed to solve the initial state estimate. When transcendental equations are involved, numeric root-finding methods are employed to find numeric solutions. In cases with linear analytic solutions, the initial state covariance can be derived explicitly. In other cases, the unscented transformation is employed to calculate the initial covariance. Two commonly used linear dynamic models and three representative nonlinear dynamic models are used as examples to illustrate the procedure of the proposed algorithm. Monte Carlo simulation results demonstrate the effectiveness of the proposed filter initialization algorithm.

跟踪性能在很大程度上取决于滤波器初始化的质量，包括准确性和一致性，尤其是在非线性滤波系统中。大多数现有方法都基于以下假设：目标位置随时间线性变化，或者动态模型是线性的。在违反假设的实际应用中，这些方法可能会导致性能下降或无法应用。为了解决这一局限性，提出了一种基于模型的线性和非线性动态系统初始化方法。根据动态模型，分析了前几次测量和要初始化的状态之间的关系，并构造了几个方程来求解初始状态估计。当涉及到超越方程时，使用数字根查找方法来找到数字解。在具有线性解析解的情况下，可以明确导出初始状态协方差。在其他情况下，采用无味变换来计算初始协方差。以两个常用的线性动力学模型和三个代表性的非线性动力学模型为例，说明了该算法的流程。蒙特卡罗仿真结果证明了所提出的滤波器初始化算法的有效性。以两个常用的线性动力学模型和三个代表性的非线性动力学模型为例，说明了该算法的流程。蒙特卡罗仿真结果证明了所提出的滤波器初始化算法的有效性。以两个常用的线性动力学模型和三个代表性的非线性动力学模型为例，说明了该算法的流程。蒙特卡罗仿真结果证明了所提出的滤波器初始化算法的有效性。