CAJ | 학술논문

实现目标数目未知且可变条件下的多目标检测与跟踪是个极具挑战性的问题，在信噪比较低的情况下更是如此。针对这一问题，该文提出一种基于点扩散模型的多目标检测前跟踪改进算法。该算法在序贯蒙特卡罗概率假设密度(SMC-PHD)滤波框架下实现，通过自适应粒子产生机制完成新生目标在像平面中的初始定位，并根据目标在图像中可能出现的位置对全体粒子集进行有效子集分割和快速权值估算，最后利用动态聚类方法完成多目标状态的准确提取。仿真结果表明，该方法有效改善了多目标检测前跟踪的估计性能，并大大提高了算法执行效率。
실현목표수목미지차가변조건하적다목표검측여근종시개겁구도전성적문제，재신조비교저적정황하경시여차。침대저일문제，해문제출일충기우점확산모형적다목표검측전근종개진산법。해산법재서관몽특잡라개솔가설밀도(SMC-PHD)려파광가하실현，통과자괄응입자산생궤제완성신생목표재상평면중적초시정위，병근거목표재도상중가능출현적위치대전체입자집진행유효자집분할화쾌속권치고산，최후이용동태취류방법완성다목표상태적준학제취。방진결과표명，해방법유효개선료다목표검측전근종적고계성능，병대대제고료산법집행효솔。
The Detection and tracking of multi-target is a challenging issue under the condition with unknown and varied target number, especially when the Signal-to-Noise Ratio (SNR) is low. An improved Track-Before-Detect (TBD) method for multiple spread targets is proposed by using point spread observation model. The method is prepared from the framework of the Sequential Monte Carlo Probability Hypothesis Density (SMC-PHD) filter, and it is implemented by firstly adopting an adaptive particle generation strategy, which can obtain the rough position estimates of the potential targets. The particle set is then partitioned into multiple subsets according to their position coordinates in 2D image plane and an efficient evaluation of the updated particle weights is accomplished by utilizing the convergence property of the particles. Target tracks are finally constructed from the extracted multitarget states via dynamic clustering technique. Simulation results show that the presented method can not only greatly improve the performance of multitarget TBD, but also significantly reduce the executing time of SMC-PHD based implementation.