利用观测历史的无线跟踪顺序发射协方差优化
Sequential Transmit Covariance Optimization for Wireless Tracking Exploiting Observation History
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中文总结 AI 辅助
本文针对MIMO雷达跟踪系统，提出利用观测历史顺序优化发射协方差矩阵，以最小化条件后验Cramér-Rao界，从而提升移动目标跟踪精度。
中文摘要 AI 辅助
本文研究了一种多输入多输出（MIMO）雷达跟踪系统，其中多天线基站（BS）旨在基于观测到的回波信号、目标状态的初始先验概率密度函数（PDF）以及状态演化模型，在多个时隙内跟踪移动目标的位置。通过利用过去时隙中已实现的观测历史，基站在收集当前回波观测之前顺序更新预测状态信息并设计发射协方差矩阵。考虑目标位置的高斯随机游走模型和复杂雷达截面（RCS）系数的高斯-马尔可夫模型，我们提出了一种有效的方法，通过高斯近似来刻画以已实现观测历史为条件的预测PDF。在此基础上，我们推导了目标状态的条件后验Fisher信息矩阵（PFIM），并进一步将目标位置状态估计的均方误差（MSE）的条件后验Cramér-Rao界（PCRB）刻画为发射协方差矩阵的显式表达式。接下来，我们构建了顺序发射协方差矩阵优化问题，以最小化每个时隙的条件PCRB。尽管该问题非凸，我们通过Schur补技术获得了最优解。数值结果表明，所提出的设计有效利用了已实现的观测历史，实现了比基准方案更低的条件PCRB，并随时间提高了跟踪精度。
英文摘要
This paper studies a multiple-input multiple-output (MIMO) radar tracking system, where a multi-antenna base station (BS) aims to track the location of a moving target over multiple time slots based on the observed echo signals, initial prior probability density function (PDF) of the target state, and state evolution model. By exploiting the realized observation history in the past time slots, the BS sequentially updates the predictive state information and designs the transmit covariance matrix before collecting the current echo observation. Considering a Gaussian random-walk model for the target location and a Gauss-Markov model for the complex radar cross-section (RCS) coefficient, we propose an effective method to characterize the predictive PDF conditioned on the realized observation history via Gaussian approximation. Based on this, we derive the conditional posterior Fisher information matrix (PFIM) for the target state, and further characterize the conditional posterior Cramér-Rao bound (PCRB) for the mean-squared error (MSE) in estimating the target's location state as an explicit expression of the transmit covariance matrix. Next, we formulate the sequential transmit covariance matrix optimization problem to minimize the conditional PCRB for each time slot. Despite the non-convexity of the problem, we obtain the optimal solution via the Schur complement technique. Numerical results show that the proposed design effectively exploits the realized observation history, achieves a lower conditional PCRB than the benchmark schemes, and improves tracking accuracy over time.
发表机构
- Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University（香港理工大学电气电子工程学系）
机构由 AI 辅助整理，请以论文原文为准。
