Track-to-track fusion of out-of-sequence tracks

Journal of Universal Computer Science

The purpose of this paper is to address some theoretical issues related to the track-to-track fusion problem when the measurements tracking the same tar- get are inherently correlated by the common process noise of the underlying target. This problem has been intensively investigated using standard Kalman filter with some appealing theoretical results, however such results are no longer valid in case of subop- timality due to either the presence of strong nonlinearity or to the discrete uncertainty pervading the origin of the measurement. This paper reviews several architectures of parallelized blocks of Kalman filters, including the augmented stacked measurement, sequential and data compression architectures. Next, convex combination architecture will be investigated and some theoretical results concerning its extension as well as in case of presence of correlation are investigated. Two special cases of correlation are highlighted. This concerns the case of presence of only two cor...

Proceedings of the 4. …, 2006

AGRIVOLTAICS2021 CONFERENCE: Connecting Agrivoltaics Worldwide

Autonomous vehicles perceive the surrounding environment by analyzing and correlating the data received from multiple sensors. The detection ability and accuracy are constrained by environmental conditions. These constraints can be overcome by fusing data from multiple sensors. But in multi-sensor fusion, Out of Sequence Measurement (OOSM) problem is increasing. In such a system, information is received at differed time at the fusion center due to different processing times of the sensors, cycle time, and communication delay which causes the later measurement from the higher rate sensor to overtake the newer measurement from the lower rate sensor. Out of sequence measurement problem can be avoided by introducing a delay in a higher rate sensor. A fixed lag algorithm is used which helps to synchronize the data at the fusion center by introducing a delay. Another approach for solving out of sequence is by predicting the data from the lower rate sensor when data from the higher rate sensor is available is done using regression. The algorithms are implemented on real-time data and the results of the fixed lag smoothing had delay hence the second approach is adaptable which will have no delay infusion.

For linear-Gaussian non-deterministic dynamics, that is, systems with non-zero process noise, it is well known that tracklet fusion based on equivalent measurement is optimal only for full communication rate, i.e., if the local posterior probabilities or estimates are communicated and fused after each observation and update time. Despite this constraint, tracklet fusion has become very popular because it performs well in many real world problems even when communication is not at full rate. By including local state estimates at multiple times, augmented state (AS) tracklet fusion computes the optimal global estimate despite this communication constraint. A similar method with this property is distributed accumulated state density (DASD) fusion, which computes decorrelated local pseudo estimates by means of a relaxed evolution model. This paper compares these two methods by examining their underlying principles. Numerical results compare their performance and also with that of a centralized Kalman filter. The results show that they have many properties such as the estimation accuracy in common despite their different derivations.

9th international conference on information fusion, 2006

The problem of track-to-track association and track fusion has been considered in the literature where the fusion center has access to multiple track estimates and the associated estimation error covariances from local sensors, as well as their crosscovariances. Due primarily to the communication constraints in real systems, some legacy trackers may only provide the local track estimates to the fusion center without any covariance information. In some cases, the local (sensor-level) trackers operate with fixed filter gain and do not have any self assessment of their estimation errors. In other cases, the network conveys a coarsely quantized root mean square (RMS) estimation error of each local tracker. Thus the fusion center needs to solve the track association and fusion problem with incomplete data from legacy local trackers.

IEEE Transactions on Aerospace and Electronic Systems, 2004

This paper presents a quantitative performance evaluation method for the maximum a posteriori (MAP) state estimate fusion algorithm. Under ideal conditions where data association is assumed to be perfect, it has been shown that the MAP or best linear unbiased estimate (BLUE) fusion formula provides the best linear minimum mean squared estimate (LMMSE) given local estimates under the linear Gaussian assumption for a static system. However, for a dynamic system where fusion is recursively performed by the fusion center on local estimates generated from local measurements, it is not obvious how the MAP algorithm will perform. In the past, several performance evaluation methods have been proposed for various fusion algorithms, including simple convex combination, cross-covariance combination, information matrix, and MAP fusion. However, not much has been done to quantify the steady state behavior of these fusion methods for a dynamic system. The goal of this work is to present analytical fusion performance results for MAP state estimate fusion without extensive Monte Carlo simulations, using an approach developed for steady state performance evaluation for track fusion. Two different communication strategies are considered: fusion with and without feedback to the sensors. Analytic curves for the steady state performance of the fusion algorithm for various communication patterns are presented under different operating conditions.

2003

Abstract Track-to-track fusion is an important part in distributed multisensor-multitarget tracking. The centralized and distributed tracking configurations were studied in (H. Chen et al., Proc. of SPIE Conf. on Signal and Data Processing of Small Targets, vol. 4048, 2000) using simulated air-to-air scenarios, and in (KC Chang, et al, IEEE Transact. on Aerospace and Electronic Systems, vol. 33, no. 4, pp. 1271-1276, 1997) with analytical results based on α-β filters.

2016 19th International Conference on Information Fusion (FUSION), 2016

We consider a target tracking problem where, in addition to the usual sensor measurements, accurate observations with uncertain timestamps are available. Such observations could, e.g., come from traces left by a target or from witnesses of an event, and have the potential in some scenarios to improve the accuracy of an estimate significantly. The Bayesian solution to the smoothing problem for one observation with uncertain timestamp is derived for a linear Gaussian state space model. The joint and marginal distributions of the states and uncertain time are derived, as well as the minimum mean squared error (MMSE) and maximum a posteriori (MAP) estimators. To attain an intuition for the problem in consideration a simple first-order example is presented and its posterior distributions and point estimators are compared and examined in some depth.

Sensors

Target detection and tracking is important in military as well as in civilian applications. In order to detect and track high-speed incoming threats, modern surveillance systems are equipped with multiple sensors to overcome the limitations of single-sensor based tracking systems. This research proposes the use of information from RADAR and Infrared sensors (IR) for tracking and estimating target state dynamics. A new technique is developed for information fusion of the two sensors in a way that enhances performance of the data association algorithm. The measurement acquisition and processing time of these sensors is not the same; consequently the fusion center measurements arrive out of sequence. To ensure the practicality of system, proposed algorithm compensates the Out of Sequence Measurements (OOSMs) in cluttered environment. This is achieved by a novel algorithm which incorporates a retrodiction based approach to compensate the effects of OOSMs in a modified Bayesian technique...

IEEE Transactions on Aerospace and Electronic Systems, 2003

Track-to-track fusion is an important part in distributed multisensor-multitarget tracking. The centralized and distributed tracking configurations were studied in using simulated air-to-air scenarios, and in with analytical results based on α-β filters. The current work generalizes the results in to the cases with more than 2 sensors. As the number of sensors increases, the performance of the distributed tracker is shown to degrade compared to the centralized estimation even when the optimal track-totrack fusion is used. An approximation technique for track-to-track fusion is also compared with the optimal approach with performance curves for various numbers of sensors. These performance curves can be used in designing a fusion system where certain trade-offs need to be considered.