基于外辐射源雷达系统的运动目标位置与速度解耦合估计方法

Decoupled Estimation Method for Position and Velocity of Moving Target Based on Passive Radar System

针对位于地球表面的无线静默目标,本文提出了一种基于外辐射源雷达系统的运动目标定位新方法.与已有大多数定位方法不同,新方法考虑了地面运动目标位置向量与速度向量所需服从的二次等式约束,并且实现了对目标位置与速度的解耦合估计.文中首先将基于外辐射源雷达系统的非线性观测方程转化为伪线性观测方程,然后利用一阶误差分析方法推导该观测方程中的误差渐近统计特性,并进而构建含双重二次等式约束的目标位置与速度联合估计准则.针对此优化模型,文中提出了一种基于拉格朗日乘子法的目标位置与速度解耦合优化算法,其中仅需对目标位置向量进行迭代计算,而目标速度向量是以闭式解的形式给出,因此可减少迭代初始值的影响与局部收敛的风险.此外,文中还在双重二次等式约束条件下推导了基于外辐射源雷达系统的运动目标定位的克拉美罗界,定量刻画了等式约束所产生的性能增益,并结合一阶误差分析与拉格朗日乘子法证明了新方法的渐近统计有效性.仿真实验结果验证了所提定位方法的优势.

In order to locate radio silent target on the Earth surface,a novel positioning method for moving target is proposed based on passive radar system.Unlike most existing positioning methods,this method takes into account the quadratic constraints that the target position vector and velocity vector need to satisfy,and can achieve decoupled estimation of the target position and velocity.Firstly,the nonlinear observation equations based on the passive radar system are transformed into pseudo-linear observation equations.Then,the asymptotic statistical properties of the errors in pseudo-linear equations are derived by applying the first-order error analysis.Subsequently,an optimization criterion is constructed for the joint estimation of the target position and velocity under the two quadratic equality constraints.For the purpose of obtaining the global optimal solution of the target position and velocity,a decoupled optimization algorithm is developed based on the Lagrange multiplier approach.This optimization algorithm requires only the iteration of the target position parameters,and the target velocity parameters can be obtained in a closed form,thus reducing the influence of initial values and the risk of local convergence.Furthermore,the Cramér-Rao bound(CRB)for moving target localization based on passive radar system is deduced under the two quadratic equality constraints,and the performance gain resulting from the equality constraints is quantified.The new estimator is also proved to be asymptotically statistically efficient by using the first-order error analysis as well as the Lagrange multiplier approach.Finally,simulation results verify the advantages of the proposed positioning method.