Traditional networked radar systems for target tracking usually suffer from a heavy data processing burden, and do not consider the sensor location uncertainties (SLUs), by assuming that radar locations are known perfectly, which is applicable only for static platforms. In this paper, considering sensors mounted on moving platforms, we propose a joint power allocation and measurement selection (JPAMS) strategy for multitarget tracking and data reduction in radar networks with the SLUs. The mechanism is to optimize the transmitted power and select the propagation paths with informative measurements, simultaneously. First, we adopt a distributed fusion architecture to estimate both states of targets and radars in clutter. Based on the distributed fusion architecture, the predicted conditional Cramér-Rao lower bound (PC-CRLB) considering the SLU and the measurement origin uncertainty is derived. Second, the JPAMS strategy is formulated as a bi-objective optimization problem, where the sum of weighted PC-CRLBs and the number of selected propagation paths are used as the performance metrics with respect to tracking and data reduction. The corresponding optimization is a NP-hard problem containing both continuous and binary variables. Third, to solve this nonconvex problem, we propose a sparsity-enhancing sequential convex programming algorithm. Finally, numerical simulations demonstrate the superiority of the proposed JPAMS strategy over the traditional allocation strategies.

中文翻译：

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具有传感器位置不确定性的雷达网络中多目标跟踪和数据缩减的资源分配

传统的用于目标跟踪的网络化雷达系统通常遭受沉重的数据处理负担，并且没有考虑传感器位置不确定性（SLU），假设雷达位置是完全已知的，这仅适用于静态平台。在本文中，考虑到安装在移动平台上的传感器，我们提出了一种联合功率分配和测量选择 (JPAMS) 策略，用于在具有 SLU 的雷达网络中进行多目标跟踪和数据缩减。该机制是优化传输功率并同时选择具有信息测量值的传播路径。首先，我们采用分布式融合架构来估计杂波中目标和雷达的状态。基于分布式融合架构，推导出考虑到 SLU 和测量原点不确定性的预测条件 Cramér-Rao 下限 (PC-CRLB)。其次，JPAMS 策略被表述为一个双目标优化问题，其中加权 PC-CRLB 的总和和所选传播路径的数量被用作跟踪和数据缩减的性能指标。相应的优化是一个包含连续变量和二元变量的 NP 难问题。第三，为了解决这个非凸问题，我们提出了一种稀疏增强的顺序凸规划算法。最后，数值模拟证明了所提出的 JPAMS 策略优于传统分配策略。其中加权 PC-CRLB 的总和和所选传播路径的数量用作关于跟踪和数据减少的性能指标。相应的优化是一个包含连续变量和二元变量的 NP 难问题。第三，为了解决这个非凸问题，我们提出了一种稀疏增强的顺序凸规划算法。最后，数值模拟证明了所提出的 JPAMS 策略优于传统分配策略。其中加权 PC-CRLB 的总和和所选传播路径的数量用作关于跟踪和数据减少的性能指标。相应的优化是一个包含连续变量和二元变量的 NP 难问题。第三，为了解决这个非凸问题，我们提出了一种稀疏增强的顺序凸规划算法。最后，数值模拟证明了所提出的 JPAMS 策略优于传统分配策略。我们提出了一种稀疏增强的顺序凸编程算法。最后，数值模拟证明了所提出的 JPAMS 策略优于传统分配策略。我们提出了一种稀疏增强的顺序凸编程算法。最后，数值模拟证明了所提出的 JPAMS 策略优于传统分配策略。