Bistatic sonobuoy deployment strategies for detecting stationary and mobile underwater targets

Abstract

The problem of determining effective allocation schemes of underwater sensors for surveillance, search, detection, and tracking purposes is a fundamental research area in military operations research. Among the various sensor types, multistatic sonobuoy systems are a promising development in submerged target detection systems. These systems consist of sources (active sensors) and receivers (passive sensors), which need not be collocated. A multistatic sonobuoy system consisting of a single source and receiver is called a bistatic system. The sensing zone of this fundamental system is defined by Cassini ovals. The unique properties and unusual geometrical profile of these ovals distinguish the bistatic sensor allocation problem from conventional sonar placement problems. This study is aimed at supporting decision makers in making the best use of bistatic sonobuoys to detect stationary and mobile targets transiting through an area of interest. We use integral geometry and geometric probability concepts to derive analytic expressions for the optimal source and receiver separation distances to maximize the detection probability of a submerged target. We corroborate our analytic results using Monte Carlo simulation. Our approach constitutes a valuable "back of the envelope" method for the important and difficult problem of analyzing bistatic sonar performance.

Three Dimensional Volume Coverage in Multistatic Sonar Sensor Networks

“…4 [50]. [24] Finding optimal solutions allowing resource control while preserving and maximizing performance for multistatic active sonobuoys in simulated non-homogeneous areas Area Coverage 2D Sonar detection Updated SCOUT program with genetic algorithm solutions [32] Determining the optimum number and placement of multistatic sonar sensors to achieve maximum coverage while minimizing the number of sensors required with a PSO-based model Area Coverage 2D Cassini oval Sequential PSO Algorithm [33] Performing analyses with sensor detection models in order to show the differences of multistatic systems compared to monostatic systems Area Coverage 2D Cassini oval Analytical model based on Cassini ovals-monostatic and multistatic detection model [34] Obtaining the best placement geometry of bistatic sonobuoys when searching for stationary and moving targets for maximizing the target detection probability Area Coverage 2D Cassini oval Analytical model based on Cassini ovals and Poisson Fields [35] To achieve maximum coverage, placement of a single source sensor for sensors with known receiver placements and investigating the effect of random and polygonal placement of receivers for different conditions.…”

“…In the Cassini oval model, the sonar equation is simplified by ignoring the spectral variation of the acoustic absorption and noise level. Consequently, the transmission loss follows the basic power rule for a constant m >0 in a homogeneous medium with spherical spreading (m = 20) [18], [22], [34]. Thus, the condition for the detecting the reflected acoustic signal is As seen in Fig.…”

“…5, b is the constant indicating the monostatic sensing distance. D 1 and D 2 are seen as the sides of the triangle near the vertex, and the distance between the source and the receiver is defined as the length of the other side of the triangle [34]. The Cassini ovals are defined as follows the source and receiver are colocated at (a, 0) [58]:…”

Using hybrid multiobjective machine learning to optimise sonobuoy placement patterns

“…In ref. [28], the effective sensor detection areas of bistatic sonobuoys are modelled as Cassini ovals in the case where a multistatic field is formed from a grid pattern of m bistatic pairs. In ref.…”

A Sensor-Network-Supported Mobile-Agent-Search Strategy for Wilderness Rescue

“…Static-sensor networks have also been proposed for search and surveillance applications [27][28][29][30][31][32][33]. These, however, have not explicitly considered dynamic scenarios where the target-location likelihood function can change over time and that the search area may expand with time.…”