On Communication-Efficient Multisensor Track Association via Measurement Transformation (Extended Version)

Multisensor track-to-track fusion for target tracking involves two primary operations: track association and estimation fusion. For estimation fusion, lossless measurement transformation of sensor measurements has been proposed for single target tracking. In this paper, we investigate track association which is a fundamental and important problem for multitarget tracking. First, since the optimal track association problem is a multi-dimensional assignment (MDA) problem, we demonstrate that MDA-based data association (with and without prior track information) using linear transformations of track measurements is lossless, and is equivalent to that using raw track measurements. Second, recent superior scalability and performance of belief propagation (BP) algorithms enable new real-time applications of multitarget tracking with resource-limited devices. Thus, we present a BP-based multisensor track association method with transformed measurements and show that it is equivalent to that with raw measurements. Third, considering communication constraints, it is more beneficial for local sensors to send in compressed data. Two analytical lossless transformations for track association are provided, and it is shown that their communication requirements from each sensor to the fusion center are less than those of fusion with raw track measurements. Numerical examples for tracking an unknown number of targets verify that track association with transformed track measurements has the same performance as that with raw measurements and requires fewer communication bandwidths

Optimal multisensor data fusion for linear systems with missing measurements

Multisensor data fusion has attracted a lot of research in recent years. It has been widely used in many applications especially military applications for target tracking and identification. In this paper, we will handle the multisensor data fusion problem for systems suffering from the possibility of missing measurements. We present the optimal recursive fusion filter for measurements obtained from two sensors subject to random intermittent measurements. The noise covariance in the observation process is allowed to be singular which requires the use of generalized inverse. Illustration example shows the effectiveness of the proposed filter in the measurements loss case compared to the available optimal linear fusion methods.<br /

Bibliographic Review on Distributed Kalman Filtering

In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman filtering (DKF) is provided.\ud The paper contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area

A new approach in distributed multisensor tracking systems based on Kalman filter methods

International audienceIn multisensor tracking systems, the state fusion also known as track to track fusion is a crucial issue where the derivation of the best track combination is obtained according to a stochastic criteria in a minimum variance sense. Recently, sub-optimal weighted combination fusion algorithms involving matrices and scalars were developed. However, hence they only depend on the initial parameters of the system motion model and noise characteristics, these techniques are not robust against erroneous measures and unstable environment. To overcome this drawbacks, this work introduces a new approach to the optimal decentralized state fusion that copes with erroneous observations and system shortcomings. The simulations results show the effectiveness of the proposed approach. Moreover, the reduced complexity of the designed algorithm is well suited for real-time implementation

A Survey on Multisensor Fusion and Consensus Filtering for Sensor Networks

Multisensor fusion and consensus filtering are two fascinating subjects in the research of sensor networks. In this survey, we will cover both classic results and recent advances developed in these two topics. First, we recall some important results in the development ofmultisensor fusion technology. Particularly, we pay great attention to the fusion with unknown correlations, which ubiquitously exist in most of distributed filtering problems. Next, we give a systematic review on several widely used consensus filtering approaches. Furthermore, some latest progress on multisensor fusion and consensus filtering is also presented. Finally, conclusions are drawn and several potential future research directions are outlined.the Royal Society of the UK, the National Natural Science Foundation of China under Grants 61329301, 61374039, 61304010, 11301118, and 61573246, the Hujiang Foundation of China under Grants C14002 and D15009, the Alexander von Humboldt Foundation of Germany, and the Innovation Fund Project for Graduate Student of Shanghai under Grant JWCXSL140

Multisensor data fusion for joint people tracking and identification with a service robot

Tracking and recognizing people are essential skills modern service robots have to be provided with. The two tasks are generally performed independently, using ad-hoc solutions that first estimate the location of humans and then proceed with their identification. The solution presented in this paper, instead, is a general framework for tracking and recognizing people simultaneously with a mobile robot, where the estimates of the human location and identity are fused using probabilistic techniques. Our approach takes inspiration from recent implementations of joint tracking and classification, where the considered targets are mainly vehicles and aircrafts in military and civilian applications. We illustrate how people can be robustly tracked and recognized with a service robot using an improved histogram-based detection and multisensor data fusion. Some experiments in real challenging scenarios show the good performance of our solution

Multisensor-based human detection and tracking for mobile service robots

The one of fundamental issues for service robots is human-robot interaction. In order to perform such a task and provide the desired services, these robots need to detect and track people in the surroundings. In the present paper, we propose a solution for human tracking with a mobile robot that implements multisensor data fusion techniques. The system utilizes a new algorithm for laser-based legs detection using the on-board LRF. The approach is based on the recognition of typical leg patterns extracted from laser scans, which are shown to be very discriminative also in cluttered environments. These patterns can be used to localize both static and walking persons, even when the robot moves. Furthermore, faces are detected using the robot's camera and the information is fused to the legs position using a sequential implementation of Unscented Kalman Filter. The proposed solution is feasible for service robots with a similar device configuration and has been successfully implemented on two different mobile platforms. Several experiments illustrate the effectiveness of our approach, showing that robust human tracking can be performed within complex indoor environments