Delayed-pilot sampling for mixture Kalman filter with application in fading channels
Delayed-pilot sampling for mixture Kalman filter with application in fading channels
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DOI：
10.1109/78.978380
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发表时间：
2002-02
期刊：
影响因子：
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通讯作者：
Xiaodong Wang;Rong Chen;D. Guo
中科院分区：
文献类型：
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作者：
Xiaodong Wang;Rong Chen;D. Guo
时序蒙特卡罗（SMC）方法是一种强大的非线性和非高斯动态系统在线滤波技术。典型的动态系统表现出强烈的记忆效应，即，未来的观察可以揭示当前状态的大量信息。在混合卡尔曼滤波器（MKF）的背景下提出了延迟采样方法，该方法利用未来的观测值来生成当前状态的样本。虽然这种方法在产生准确的滤波结果方面非常有效，但由于需要将未来状态边缘化，其计算复杂度在延迟方面呈指数级增长。我们通过开发两种新的延迟估计采样方案来解决这一困难，即延迟导频采样和混合导频采样。延迟导频采样的基本思想是，我们不是探索未来状态的整个空间，而是生成许多随机导频流，每个导频流都表明，如果当前状态取特定值，未来会发生什么。然后，当前状态的抽样分布由与每个导流相关联的增量重要性权重决定。延迟导频采样可以与延迟采样方法结合使用，形成一种混合方案。然后将这种新的采样技术应用于解决平坦衰落通信信道中的自适应检测和解码问题。仿真结果验证了这种低复杂度采样方法的延迟估计性能，并与延迟采样方法进行了比较。
Sequential Monte Carlo (SMC) methods are powerful techniques for online filtering of nonlinear and non-Gaussian dynamic systems. Typically dynamic systems exhibit strong memory effects, i.e., future observations can reveal substantial information about the current state. The delayed-sample sampling method has been proposed in the context of a mixture Kalman filter (MKF), which makes use of future observations in generating samples of the current state. Although this method is highly effective in producing accurate filtering results, its computational complexity is exponential in terms of the delay, due to the need to marginalize the future states. We address this difficulty by developing two new sampling schemes for delayed estimation, namely, delayed-pilot sampling and hybrid-pilot sampling. The basic idea of delayed-pilot sampling is that instead of exploring the entire space of future states, we generate a number of random pilot streams, each of which indicates what would happen in the future if the current state takes a particular value. The sampling distribution of the current state is then determined by the incremental importance weight associated with each pilot stream. The delayed-pilot sampling can be used in conjunction with the delayed-sample method, resulting in a hybrid scheme. This new sampling technique is then applied to solve the problem of adaptive detection and decoding in flat-fading communication channels. Simulation results are provided to demonstrate the performance of the new low-complexity sampling techniques for delayed estimation and for comparison with the delayed-sample method.