CAJ | 학술논문

针对密集杂波条件下的目标检测与跟踪问题，开展极大似然概率数据关联（maximum likelihood-probabilistic data association，ML-PDA）算法优化与实时计算问题研究。在算法层面，通过在极大化对数似然比（log likelihood ratio，LLR）过程中引入粒子群优化（particle swarm optimization，PSO）方法，并进一步提出基于观测引导的 PSO 播撒粒子方式，提升算法的计算效率；在实现层面，提出基于图形处理器（graphic processing unit， GPU）的 PSO 实现策略。仿真实验结果说明了基于观测引导 PSO 算法搜索的有效性。在 GPU 平台上实现该算法获得显著的加速比，验证了所提出方法具有工程实时性。
침대밀집잡파조건하적목표검측여근종문제，개전겁대사연개솔수거관련（maximum likelihood-probabilistic data association，ML-PDA）산법우화여실시계산문제연구。재산법층면，통과재겁대화대수사연비（log likelihood ratio，LLR）과정중인입입자군우화（particle swarm optimization，PSO）방법，병진일보제출기우관측인도적 PSO 파살입자방식，제승산법적계산효솔；재실현층면，제출기우도형처리기（graphic processing unit， GPU）적 PSO 실현책략。방진실험결과설명료기우관측인도 PSO 산법수색적유효성。재 GPU 평태상실현해산법획득현저적가속비，험증료소제출방법구유공정실시성。
The target detection and tracking problems when involved in high dense clutter are addressed. Specifically,we propose to solve the optimization and computation problems of maximum likelihood-probabilistic data association (ML-PDA).The particle swarm optimization (PSO)algorithm to maximize the log likelihood ratio (LLR)is adopted.We propose to initialize the particles of PSO based on measurements,which improves the computation efficiency.Furthermore,we propose a scheme which allows implementing PSO in parallel on graphic processing unit (GPU).The efficiency of the proposed algorithm and the parallelized scheme are illus-trated based on simulations.