In this paper, we present our study on track classification by taking into account environmental information and target estimated states. The tracker uses several motion model adapted to different target dynamics (pedestrian, ground vehicle and SUAV, i.e. small unmanned aerial vehicle) and works in centralized architecture. The main idea is to explore both: classification given by heterogeneous sensors and classification obtained with our fusion module. The fusion module, presented in his paper, provides a class on each track according to track location, velocity and associated uncertainty. To model the likelihood on each class, a fuzzy approach is used considering constraints on target capability to move in the environment. Then the evidential reasoning approach based on Dempster-Shafer Theory (DST) is used to perform a time integration of this classifier output. The fusion rules are tested and compared on real data obtained with our wireless sensor network.In order to handle realistic ground target tracking scenarios, we use an autonomous smart computer deposited in the surveillance area. After the calibration step of the heterogeneous sensor network, our system is able to handle real data from a wireless ground sensor network. The performance of this system is evaluated in a real exercise for intelligence operation (“hunter hunt” scenario).
In this paper, we address the problem of multiple ground target tracking and classification with information obtained from a unattended wireless sensor network. A multiple target tracking (MTT) algorithm, taking into account road and vegetation information, is proposed based on a centralized architecture. One of the key issue is how to adapt classical MTT approach to satisfy embedded processing. Based on track statistics, the classification algorithm uses estimated location, velocity and acceleration to help to classify targets. The algorithms enables tracking human and vehicles driving both on and off road. We integrate road or trail width and vegetation cover, as constraints in target motion models to improve performance of tracking under constraint with classification fusion. Our algorithm also presents different dynamic models, to palliate the maneuvers of targets. The tracking and classification algorithms are integrated into an operational platform (the fusion node). In order to handle realistic ground target tracking scenarios, we use an autonomous smart computer deposited in the surveillance area. After the calibration step of the heterogeneous sensor network, our system is able to handle real data from a wireless ground sensor network. The performance of system is evaluated in a real exercise for intelligence operation ("hunter hunt" scenario).
In this contribution, we propose to improve the grid map occupancy estimation method developed so far based on belief function modeling and the classical Dempster’s rule of combination. Grid map offers a useful representation of the perceived world for mobile robotics navigation. It will play a major role for the security (obstacle avoidance) of next generations of terrestrial vehicles, as well as for future autonomous navigation systems. In a grid map, the occupancy of each cell representing a small piece of the surrounding area of the robot must be estimated at first from sensors measurements (typically LIDAR, or camera), and then it must also be classified into different classes in order to get a complete and precise perception of the dynamic environment where the robot moves. So far, the estimation and the grid map updating have been done using fusion techniques based on the probabilistic framework, or on the classical belief function framework thanks to an inverse model of the sensors. Mainly because the latter offers an interesting management of uncertainties when the quality of available information is low, and when the sources of information appear as conflicting. To improve the performances of the grid map estimation, we propose in this paper to replace Dempster’s rule of combination by the PCR6 rule (Proportional Conflict Redistribution rule #6) proposed in DSmT (Dezert-Smarandache) Theory. As an illustrating scenario, we consider a platform moving in dynamic area and we compare our new realistic simulation results (based on a LIDAR sensor) with those obtained by the probabilistic and the classical belief-based approaches.
In this paper, we address the problem of multiple ground target tracking and classification with data from an unattended wireless sensor network. A multiple target tracking algorithm, taking into account the road and vegetation information, is studied in a centralized architecture. Despite of efficient algorithms proposed in the literature, we must adapt a basic approach to satisfy embedded processing. The algorithm enables tracking human and vehicles driving both on and off road. Based on our previous works, we integrate road or trail width and vegetation cover, in motion model to improve performance of tracking under constraint. Our algorithm also presents different dynamic models, to palliate the maneuvers of targets including a stop motion model. In order to handle realistic ground target tracking scenarios, the tracking algorithm is integrated into an operational platform (named fusion node) which is an autonomous smart computer abandoned in the surveillance area. After the calibration step of the heterogeneous sensor network, our system is able to handle real data from a wireless ground sensor network. The performance of system is evaluated in a real exercise for Forward Operating Base (FOB) protection and road surveillance.
In this paper, data obtained from wireless unattended ground sensor network are used for tracking multiple ground targets (vehicles, pedestrians and animals) moving on and off the road network. The goal of the study is to evaluate several data fusion algorithms to select the best approach to establish the tactical situational awareness. The ground sensor network is composed of heterogeneous sensors (optronic, radar, seismic, acoustic, magnetic sensors) and data fusion nodes. The fusion nodes are small hardware platforms placed on the surveillance area that communicate together. In order to satisfy operational needs and the limited communication bandwidth between the nodes, we study several data fusion algorithms to track and classify targets in real time. A multiple targets tracking (MTT) algorithm is integrated in each data fusion node taking into account embedded constraint. The choice of the MTT algorithm is motivated by the limit of the chosen technology. In the fusion nodes, the distributed MTT algorithm exploits the road network information in order to constrain the multiple dynamic models. Then, a variable structure interacting multiple model (VS-IMM) is adapted with the road network topology. This algorithm is well-known in centralized architecture, but it implies a modification of other data fusion algorithms to preserve the performances of the tracking under constraints. Based on such VS-IMM MTT algorithm, we adapt classical data fusion techniques to make it working in three architectures: centralized, distributed and hierarchical. The sensors measurements are considered asynchronous, but the fusion steps are synchronized on all sensors. Performances of data fusion algorithms are evaluated using simulated data and also validated on real data. The scenarios under analysis contain multiple targets with close and crossing trajectories involving data association uncertainties.
Over the years, there have been many proposed methods in set-based tracking. One example of set-based methods is the use
of Dempster-Shafer (DS) techniques to support belief-function (BF) tracking. In this paper, we overview the issues and
concepts that motivated DS methods for simultaneous tracking and classification/identification. DS methods have some
attributes, if applied correctly; but there are some pitfalls that need to be carefully avoided such as the redistribution of the
mass associated with conflicting measurements. Such comparisons and applications are found in Dezert-Smarandache
Theory (DSmT) methods from which the Proportional Conflict Redistribution (PCR5) rule supports a more comprehensive
approach towards applying evidential and BF techniques to target tracking. In the paper, we overview two decades of
research in the area of BF tracking and conclude with a comparative analysis of Bayesian, Dempster-Shafer, and the PCR5
In the battlefield surveillance domain, ground target tracking is used to evaluate the threat. Data used for
tracking is given by a Ground Moving Target Indicator (GMTI) sensor which only detects moving targets.
Multiple target tracking has been widely studied but most of the algorithms have weaknesses when targets are
close together, as they are in a convoy. In this work, we propose a filtering approach for convoys in the midst of
civilian traffic. Inspired by particle filtering, our specific algorithm cannot be applied to all the targets because of
its complexity. That is why well discriminated targets are tracked using an Interacting Multiple Model-Multiple
Hypothesis Tracking (IMM-MHT), whereas the convoy targets are tracked with a specific particle filter. We
make the assumption that the convoy is detected (position and number of targets). Our approach is based on an
Independent Partition Particle Filter (IPPF) incorporating constraint-regions. The originality of our approach
is to consider a velocity constraint (all the vehicles belonging to the convoy have the same velocity) and a group
constraint. Consequently, the multitarget state vector contains all the positions of the individual targets and
a single convoy velocity vector. When another target is detected crossing or overtaking the convoy, a specific
algorithm is used and the non-cooperative target is tracked down an adapted particle filter. As demonstrated
by our simulations, a high increase in convoy tracking performance is obtained with our approach.
The Tracking of a Ground Moving Target (GMTI) is a challenging problem given the environment complexity, the target maneuvers and the false alarm rate. Using the road network information in the tracking process is considered an asset mainly when the target movement is limited to the road. In this paper, we consider different approaches to incorporate the road information into the tracking process: Based on the assumption that the target is following the road network and using a classical estimation technique, the idea is to keep the state estimate on the road by using different “projections” approaches. The first approach is a deterministic one based either on the minimization of the distance between the estimate and its projection on the road or on the minimization of the distance between the measurement and its projection on the road. In this case, the state estimate is updated using the projected measurement. The second approach is a probabilistic one. Given the probability distributions of the measurement error and the state estimate, we propose to use this information in order to maximize the a posteriori measurement probability and the a posteriori estimate probability under the road constraints. This maximization is equivalent to a minimization of the Mahalanobis distance under the same constraints. To differentiate this approach from the deterministic one, we called the projection pseudo projection on the road segment. In this paper, we present a comparative study of the performances of these projection approaches for a simple tracking case. Then we extend the study to the case of road intersections in which we present a sequential ratio test in order to select the best road segment.