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

Location Estimation in a Smart Home: System Implementation and Evaluation Using Experimental Data

In the context of a constantly increasing aging population with cognitive deficiencies, insuring the autonomy of the elders at home becomes a priority. The DOMUS laboratory is addressing this issue by conceiving a smart home which can both assist people and preserve their quality of life. Obviously, the ability to monitor properly the occupant's activities and thus provide the pertinent assistance depends highly on location information inside the smart home. This paper proposes a solution to localize the occupant thanks to Bayesian filtering and a set of anonymous sensors disseminated throughout the house. The localization system is designed for a single person inside the house. It could however be used in conjunction with other localization systems in case more people are present. Our solution is functional in real conditions. We conceived an experiment to estimate precisely its accuracy and evaluate its robustness. The experiment consists of a scenario of daily routine meant to maximize the occupant's motion in meaningful activities. It was performed by 14 subjects, one subject at a time. The results are satisfactory: the system's accuracy exceeds 85% and is independent of the occupant's profile. The system works in real time and behaves well in presence of noise

FISST Based Method for Multi-Target Tracking in the Image Plane of Optical Sensors

A finite set statistics (FISST)-based method is proposed for multi-target tracking in the image plane of optical sensors. The method involves using signal amplitude information in probability hypothesis density (PHD) filter which is derived from FISST to improve multi-target tracking performance. The amplitude of signals generated by the optical sensor is modeled first, from which the amplitude likelihood ratio between target and clutter is derived. An alternative approach is adopted for the situations where the signal noise ratio (SNR) of target is unknown. Then the PHD recursion equations incorporated with signal information are derived and the Gaussian mixture (GM) implementation of this filter is given. Simulation results demonstrate that the proposed method achieves significantly better performance than the generic PHD filter. Moreover, our method has much lower computational complexity in the scenario with high SNR and dense clutter

Multi-target pig tracking algorithm based on joint probability data association and particle filter

In order to evaluate the health status of pigs in time, monitor accurately the disease dynamics of live pigs, and reduce the morbidity and mortality of pigs in the existing large-scale farming model, pig detection and tracking technology based on machine vision are used to monitor the behavior of pigs. However, it is challenging to efficiently detect and track pigs with noise caused by occlusion and interaction between targets. In view of the actual breeding conditions of pigs and the limitations of existing behavior monitoring technology of an individual pig, this study proposed a method that used color feature, target centroid and the minimum circumscribed rectangle length-width ratio as the features to build a multi-target tracking algorithm, which based on joint probability data association and particle filter. Experimental results show the proposed algorithm can quickly and accurately track pigs in the video, and it is able to cope with partial occlusions and recover the tracks after temporary loss

Evaluation and extensions of the probabilistic multi-hypothesis tracking algorithm to cluttered environments

This research examines the probabilistic multi-hypothesis tracker (PHMT), a batch mode, empirical, Bayesian data association and tracking algorithm. Like a traditional multi-hypothesis tracker (MHT), track estimation is deferred until more conclusive data is gathered. However, unlike a traditional algorithm, PMHT does not attempt to enumerate all possible combinations of feasible data association links, but uses a probabilistic structure derived using expectation maximization. This study focuses on two issues: the behavior of the PMHT algorithm in clutter and algorithm initialization in clutter. We also compare performance between this algorithm and other algorithms, including a nearest neighbor tracker, a probabilistic data association filter (PDAF), and a traditional measurement oriented MHT algorithm.Naval Undersea Warfare CenterApproved for public release; distribution is unlimited