Hierarchical path computation approach for large graphs

Time is a critical factor in several path planning problems such as flood emergency rescue operations, escape planning from fires and chemical warfare agents dispersed in large buildings, evacuation from urban areas during natural disasters such as earthquakes, and military personnel movement. We propose a hierarchical path planning algorithm (HIPLA) for real time path planning problems where the computational time is of critical significance. The main idea of HIPLA is to significantly reduce the search space for path computation by searching in a high-level abstraction graph, whose nodes are associated with precomputed risk estimates. The cumulative risk associated with all nodes along a path determines the quality of a path. We present a detailed experimental analysis of HIPLA by comparing it with two well-known approaches viz., shortest path algorithm (SPAH) [1] and Dijkstra's algorithm with pruning [2] for large node-weighted graphs.     

Temporally staggered sensors in multi-sensor target tracking systems

For a multi-sensor target tracking system, the effects of temporally staggered sensors on system performance are investigated and compared with those of synchronous sensors. To capture system performance over time, a new metric, the average estimation error variance (AEV), is proposed. For a system that has N sensors with equal measurement noise variance, numerical results show that the optimal staggering pattern is to use N uniformly staggered sensors. We have also shown analytically that the AEV of the system with N uniformly staggered sensors is always smaller than that of the system with N synchronous sensors. For sensors with different measurement noise variances, the optimal staggering pattern can be found numerically. Practical guidelines on selecting the optimal staggering pattern have been presented for different target tracking scenarios. Due to its simplicity, uniform staggering can be used as an alternative scheme with relatively small performance degradation.     

A jerk model for tracking highly maneuvering targets

A model of target motion in three-dimensional space that includes position derivatives up to the third order is developed. Compared with available models, which include terms at the most up to the second derivative, the model introduced in this work, called the jerk model, can more accurately describe agile target maneuvers which are likely to contain significant higher order derivatives. A compatible 4-state Kalman filter to perform tracking in conjunction with the jerk model is also presented, and an initialization procedure for the filter is provided. The improved performance of the jerk model over a lower order model is illustrated through numerical simulation.