Some assignment problems arising from multiple target tracking

Combinatorial Optimization, 2000

The ever-increasing demand in surveillance is to produce highly accurate target and track identification and estimation in real-time, even for dense target scenarios and in regions of high track contention. The use of multiple sensors, through more varied information, has the potential to greatly enhance target identification and state estimation. For multitarget tracking, the processing of multiple scans all at once yields high track identification. However, to achieve this accurate state estimation and track identification, one must solve an NP-hard data association problem of partitioning observations into tracks and false alarms in real-time. Over the last ten years a new approach to the problem formulation based on multidimensional assignment problems and near optimal solution in real-time by Lagrangian relaxation has evolved and is proving to be superior to all other approaches. This work reviews the problem formulation and algorithms with some suggested future directions.

IEEE Transactions on Aerospace and Electronic Systems, 2001

We present the development of a multisensor fusion algorithm using multidimensional data association for multitarget tracking. The work is motivated by a large scale surveillance problem, where observations from multiple asynchronous sensors with time-varying sampling intervals (electronically scanned array (ESA) radars) are used for centralized fusion. The combination of multisensor fusion with multidimensional assignment is done so as to maximize the "time-depth,'' in addition to "sensor-width" for the number S of lists handled by the assignment algorithm. The standard procedure, which associates measurements from the most recently arrived S -I frames to established tracks, can have, in the case of S sensors, a time-depth of zero. A new technique, which guarantees maximum effectiveness for an S-dimensional data association (S 2 3), i.e., maximum time-depth (S -I) for each sensor without sacrificing the fusion across sensors, is presented.

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IEEE Transactions on Aerospace and Electronic Systems, 2001

In this paper we describe a novel data association algorithm, termed m-best S-D, that determines in O(mSkn 3 ) time (m assignments, S¸3 lists of size n, k relaxations) the (approximately) m-best solutions to an S-D assignment problem. The m-best S-D algorithm is applicable to tracking problems where either the sensors are synchronized or the sensors and/or the targets are very slow moving. The significance of this work is that the m-best S-D assignment algorithm (in a sliding window mode) can provide for an efficient implementation of a suboptimal multiple hypothesis tracking (MHT) algorithm by obviating the need for a brute force enumeration of an exponential number of joint hypotheses.

Journal of Intelligent & Robotic Systems, 2012

When associating data in the context of multiple target tracking, one is faced with the problem of handling the target emergence and disappearance. In this paper we show that we are able to handle this issue using belief theory based data association method without the introduction of an additional hypothesis to the frame of discernment. Using a specific modelling of belief functions, this is done by detecting and managing a portion of a conflict, which originates from the non-exhaustivity of the frame of discernment. The proposed method is associative and does not rely on the order under which the beliefs are combined. We demonstrate the effectiveness of the proposed method with experiments on simulated data. Additionally, we compare it with the extended world based data association method where a virtual hypothesis is added to the frame of discernment.

2012

Multiple target tracking that integrates target model estimation and data association steps is described. The integration allows successive refinement of the models while reducing the uncertainty in data association. Each target is described by "weak" models of kinematics, shape and appearance. The target models are refined in a two-stage process: imagebased tracklets of high purity and accuracy are generated, and geospatial tracks are extended from these tracklets. During each stage of tracking, observation data of reduced uncertainties are associated with the refined tracks in a probabilistic manner. We describe our approach in the context of a real time system that has been tested and evaluated for vehicle and human tracking in sparse, medium, and dense clutter using aerial EO/IR video.

… and Control, 2004. CDC. 43rd IEEE …, 2006

The problem of track-to-track association has been considered until recently in the literature only for pairwise associations. In view of the extensive recent interest in multisensor data fusion, the need to associate simultaneously multiple tracks has arisen. This is due primarily to bandwidth constraints in real systems, where it is not feasible to transmit detailed measurement information to a fusion center but, in many cases, only local tracks. As it has been known in the literature, tracks of the same target obtained from independent sensors are still dependent due to the common process noise [2]. This paper derives the exact likelihood function for the track-totrack association problem from multiple sources, which forms the basis for the cost function used in a multidimensional assignment algorithm that can solve such a large scale problem where many sensors track many targets. While a recent work [14] derived the likelihood function under the assumption that the track errors are independent, the present paper incorporates the (unavoidable) dependence of these errors.

Computers & Operations Research, 2003

In this work we present a linear programming (LP) based approach for solving the data association problem (DAP) in multiple target tracking. It is well-known that the DAP can be formulated as an integer program. We present a compact formulation of the DAP. To solve practical instances of the DAP we propose an algorithm that uses an iterated K-scan sliding window technique. In each iteration we solve the LP relaxation of an integer program and next apply a greedy rounding procedure. Computational experiments indicate that the quality of the solutions found is quite satisfactory.

AIAA Guidance, Navigation, and Control Conference and Exhibit, 2003

Tracking in multi sensor multi target (MSMT) scenario is a complex problem due to the uncertainties in the origin of observations. Solution to this problem requires appropriate gating and data association procedures to associate measurements with targets. A PC MATLAB program based on track-oriented approach is evaluated which uses nearest neighbor Kalman filter (NNKF) and probabilistic data association filter (PDAF) for tracking multiple targets from data of multiple sensors. For track-to-track fusion, state vector fusion philosophy is employed. The tracking performance in the presence of simulated track loss and recovery as well as in clutter is evaluated. During data loss PDAF performed better than NNKF. In the presence of mild clutter and sparse target scenarios, the NNKF and PDAF give similar performance.

IEEE Aerospace and Electronic Systems Magazine, 2000

Target tracking using multiple sensors can provide better performance than using a single sensor. One approach to multiple target tracking with multiple sensors is to first perform single sensor tracking and then fuse the tracks from the different sensors. Two processing architectures for track fusion are presented: sensor to sensor track fusion, and sensor to system track fusion. Technical issues related to the statistical correlation between track estimation errors are discussed. Approaches for associating the tracks and combining the track state estimates of associated tracks that account for this correlation are described and compared by both theoretical analysis and Monte Carlo simulations.