CAJ | 학술논문

　　该文在扩展卡尔曼粒子滤波算法的基础上融合了“负”信息(没有接收到观测值的扫描)来实现远距离干扰环境下的目标跟踪。在整个实现过程中，由传感器模型推导出的高斯和似然函数充分考虑了正负信息，直接用于计算粒子权重更新。并且通过扩展卡尔曼滤波算法产生重要性密度函数，利用当前时刻的量测，使得粒子的分布更接近其后验概率分布，而且使用较少的粒子个数即可达到较好的跟踪效果。仿真证明，扩展卡尔曼粒子滤波算法在航迹连续性和跟踪精度方面明显优于扩展卡尔曼滤波算法，但计算复杂度较高。
　　해문재확전잡이만입자려파산법적기출상융합료“부”신식(몰유접수도관측치적소묘)래실현원거리간우배경하적목표근종。재정개실현과정중，유전감기모형추도출적고사화사연함수충분고필료정부신식，직접용우계산입자권중경신。병차통과확전잡이만려파산법산생중요성밀도함수，이용당전시각적량측，사득입자적분포경접근기후험개솔분포，이차사용교소적입자개수즉가체도교호적근종효과。방진증명，확전잡이만입자려파산법재항적련속성화근종정도방면명현우우확전잡이만려파산법，단계산복잡도교고。
An Extended Kalman Particle Filter (EKPF) integrated with “negative” information (scans or dwells with no measurements) is implemented for target tracking in the Stand-Off Jammer (SOJ). In the EKPF, the Gaussian sum likelihood function which is derived from a sensor model accounting for both the positive information and negative information is directly used in the weight update of the particle filter. And the importance density function is generated by using the Extended Kalman Filter (EKF) to take full account of the current measurement, thus leading to the distribution of the particles approaching the posterior probability density function. Moreover, use of a small number of particles can achieve good tracking accuracy. Simulation results show that EKPF outperforms the EKF implementation in terms of track continuity and track accuracy but at the cost of large computation complexity.