论文标题
基于线性TDOA的分布式估计和局部跟踪的测量值
Linear TDOA-based Measurements for Distributed Estimation and Localized Tracking
论文作者
论文摘要
我们提出了一个线性时间差异（TDOA）测量模型，以改善局部目标跟踪的\ textIt {分布式}估计性能。我们使用基于共识的数据融合技术在稀疏（可能是大规模的）通信网络上设计分布式过滤器。提议的分布式和局部跟踪协议大大降低了传感器网络所需的连接和通信速率。此外，如果失去通信链接或传感器节点，我们考虑$κ$冗余的可观察性和容忍度的设计。我们介绍其余传感器网络（链接/节点删除后）上的最小条件，以便仍然保留分布式可观察性，因此，传感器网络可以跟踪（单个）操纵目标。动机是减少通信负载与处理负载，因为一般而言，计算单元的成本少于通信设备。我们通过MATLAB中的模拟评估跟踪性能。
We propose a linear time-difference-of-arrival (TDOA) measurement model to improve \textit{distributed} estimation performance for localized target tracking. We design distributed filters over sparse (possibly large-scale) communication networks using consensus-based data-fusion techniques. The proposed distributed and localized tracking protocols considerably reduce the sensor network's required connectivity and communication rate. We, further, consider $κ$-redundant observability and fault-tolerant design in case of losing communication links or sensor nodes. We present the minimal conditions on the remaining sensor network (after link/node removal) such that the distributed observability is still preserved and, thus, the sensor network can track the (single) maneuvering target. The motivation is to reduce the communication load versus the processing load, as the computational units are, in general, less costly than the communication devices. We evaluate the tracking performance via simulations in MATLAB.
