Technical note: A computationally efficient algorithm for undiscounted Markov decision processes with restricted observations

We present a computationally efficient procedure to determine control policies for an infinite horizon Markov Decision process with restricted observations. The optimal policy for the system with restricted observations is a function of the observation process and not the unobservable states of the system. Thus, the policy is stationary with respect to the partitioned state space. The algorithm we propose addresses the undiscounted average cost case. The algorithm combines a local search with a modified version of Howard's (Dynamic programming and Markov processes, MIT Press, Cambridge, MA, 1960) policy iteration method. We demonstrate empirically that the algorithm finds the optimal deterministic policy for over 96% of the problem instances generated. For large scale problem instances, we demonstrate that the average cost associated with the local optimal policy is lower than the average cost associated with an integer rounded policy produced by the algorithm of Serin and Kulkarni Math Methods Oper Res 61 (2005) 311–328. © 2008 Wiley Periodicals, Inc. Naval Research Logistics 2009     

相控阵雷达LPI搜索方法研究

相控阵雷达可通过合理配置工作参数优化其性能。针对相控阵雷达在搜索目标时易受敌方电子侦察设备威胁的现实,研究了在保持搜索能力的同时,通过合理配置参数降低截获概率的问题。分析了影响相控阵雷达搜索状态LPI(Low Probability of Intercept)性能的主要因素,建立了截获概率模型与搜索能力模型,给出了3种参数调整策略。仿真试验验证了模型的有效性,并比较了3种策略的优劣。