一种红外多传感器对中段弹道空间邻近目标的联合超分辨弹道估计方法

An Algorithm of Joint Super-resolution and Ballistic Trajectory Estimation for Midcourse Ballistic Closely Spaced Objects by Infrared Multi-sensor

分析红外焦平面(IRFRA)对中段弹道空间邻近目标(CSO)的成像特点,指出星载红外传感器为实现对空间邻近目标的跟踪必须对其进行超分辨。提出了一种中段弹道空间邻近目标联合超分辨与弹道估计新方法。该方法结合红外焦平面成像模型和中段弹道动力学模型,使得能够同时利用红外多传感器的多帧信息,基于最小二乘准则建立联合超分辨弹道估计目标函数,并分析选择各目标的起始状态参数作为模型参数。针对目标函数的高维非线性特点,推导最小化意义下等价的降维目标函数,采用量子粒子群优化算法最优化该降维目标函数直接求解模型参数,进而计算出各目标的弹道和辐射强度,实现中段弹道空间邻近目标的联合超分辨与弹道估计。仿真结果验证了该方法的有效性,且相比于传统的先单传感器单帧超分辨、然后多传感器多帧测角数据关联与滤波方法,新方法在避免数据关联复杂问题的同时,其弹道估计精度更高、分辨能力更强。

The imaging characteristics of midcourse ballistic closely spaced objects （CSO） on an infrared focal plane array （IR FPA） are analyzed,and it is pointed out that space-based infrared sensors should super-resolute CSO while tracking on them.A novel model-based algorithm of joint super-resolution and ballistic trajectory estimation for CSO is presented which combines models of IR FPA imaging and midcourse ballistic dynamics,and establishes an objective function of joint super-resolution and trajectory estimation based on the least squares criteria from space and time information.In addition,CSO initial state parameters are selected as model parameters by analysis.To cope with the high-dimensional and nonlinear characteristics of the original objective function,a dimension-decreased function is inferred,and then quantum-behaved particle swarm optimization is included to estimate directly the model parameters,and subsequently the trajectory and radiant intensity of the objects are calculated,thus ultimately realizing midcourse CSO joint super-resolution and ballistic trajectory estimation.Simulation results confirm the effectiveness of the algorithm.Meanwhile,the results also show that,in contrast to traditional methods,the new algorithm not only avoids complex data association but also gains stronger resolution capability and higher precision in location.