Tracking Algorithms for Multistatic Sonar Systems

Abstract

Activated reconnaissance systems based on target illumination are of high importance for surveillance tasks where targets are nonemitting. Multistatic configurations, where multiple illuminators and multiple receivers are located separately, are of particular interest. The fusion of measurements is a prerequisite for extracting and maintaining target tracks. The inherent ambiguity of the data makes the use of adequate algorithms, such as multiple hypothesis tracking, inevitable. For their design, the understanding of the residual clutter, the sensor resolution and the characteristic impact of the propagation medium is important. This leads to precise sensor models, which are able to determine the performance of the surveillance team. Incorporating these models in multihypothesis tracking leads to a situationally aware data fusion and tracking algorithm. Various implementations of this algorithm are evaluated with the help of simulated and measured data sets. Incorporating model knowledge leads to increased performance, but only if the model is in line with the physical reality: we need to find a compromise between refined and robust tracking models. Furthermore, to implement the model, which is inherently nonlinear for multistatic sonar, approximations have to be made. When engineering the multistatic tracking system, sensitivity studies help to tune model assumptions and approximations.

Batch tracking algorithm for multistatic sonars

“…As for N n , it denotes the n-dimensional Gaussian distribution. Observe that the above assumptions are quite reasonable because of several sources of error that affect the target-originated contact parameters [14]. For example, the TDOA suffers from insufficient synchronisation between sources and receivers, distance calculations suffer from insufficient knowledge of the sound speeds and accordingly sound paths.…”

“…As for the noisy measurements, the standard deviations of target-originated measurements are set as follows These values are in the similar range of values extracted from real measurements [14] and they are also commonly used in the multistatic tracking community [21]. On the other hand, for each couple source-receiver the false contacts are distributed uniformly in the smallest bistatic ellipse that contains the surveillance region.…”

“…The underwater surveillance system consists of multiple acoustic sound sources and multiple receivers. This setup is called multistatic sonar and leads to an increased detection performance and accuracy in target state estimation [14]. The sensors make hard decisions and transmit the measurements associated with each contact (for sonar applications, a target detection is usually referred to as a contact) (including side information) to the fusion centre for track estimate.…”

Multi-agent Teams for Underwater Surveillance

“…As written in step 2) and 4) tracking is a major part of the multistatic software: In fact, previous research has shown that tracking algorithms like multi-hypotheses tracking (MHT) not only provide a very elegant framework to perform this task, but that, even more, the "integration of information" over time is necessary to actually do the association, i.e. kinematic information is necessary to associate contacts in the stochastically fluctuating sonar pictures [11].…”

“…The quality of each virtSB can be calculated in the same manner as for the sensor model in a multistatic tracker [4]. In [11], measurement uncertainties and the detection performance are appropriately modeled.…”