Adaptive ping control for track-holding in multistatic active sonar networks

Abstract

Distributed multistatic active sonar networks provide an Anti-Submarine Warfare capability against small, quiet, threat submarines in the harsh clutter-saturated littoral and deeper ocean environments. Adaptive ping control techniques provide the potential to significantly increase the multistatic network's performance, by pinging (in an optimum sense) the right source, at the right time, with the right waveform. This paper describes an automatic, adaptive ping control algorithm. It specifically addresses the "trackhold" objective, which is to adapt multistatic sonar operations to maintain and hold one or more target tracks which have been previously initiated (detected). The approach is unique in that it includes both sonar performance modeling and multistatic tracker outputs, in a closed-loop control structure. The paper motivates the approach, describes the algorithm, and shows some validating results. The evaluation utilizes a simple sonar performance model, a ping contact simulator, and a multistatic target tracker. Results are shown for a simple simulated scenario, showing the advantages of this adaptive ping control algorithm compared to using a preplanned, non-adaptive ping transmission schedule.

Bistatic sonobuoy deployment strategies for detecting stationary and mobile underwater targets

“…In more recent studies, Angley et al (, ) develop efficient ping scheduling methodologies for multistatic sonars that seek to improve tracking performance. Some other studies that consider the ping scheduling problem for bistatic and multistatic sonar include Wakayama and Grimmett (), Wakayama, Zabinsky, and Grimmett (), and Suvorova, Morelande, Moran, Simakov, and Fletcher ().…”

Non‐myopic sensor scheduling for multistatic sonobuoy fields

“…Greedy scheduling algorithms define a reward for all possible actions a single step ahead and pick the action that maximises this reward. Significant work has since been undertaken to define new metrics for sensor scheduling using greedy algorithms [1,[6][7][8][9][10][11]. Greedy algorithms have the advantage of being computationally efficient, but can have issues effectively prioritising resources in complex multi-target scenarios, especially, as in this case, when conserving the battery power of the sonobuoys is important.…”