Multi‐sensor track‐to‐track fusion via linear minimum variance sense estimators

Abstract

An integrated approach that consists of sensor-based filtering algorithms, local processors, and a global processor is employed to describe the distributed fusion problem when several sensors execute surveillance over a certain area. For the sensor tracking systems, each filtering algorithm utilized in the reference Cartesian coordinate system is presented for target tracking, with the radar measuring range, bearing, and elevation angle in the spherical coordinate system (SCS). For the local processors, each track-to-track fusion algorithm is used to merge two tracks representing the same target. The number of 2combinations of a set with N distinct sensors is considered for central track fusion. For the global processor, the data fusion algorithms, simplified maximum likelihood (SML) estimator and covariance matching method (CMM), based on linear minimum variance (LMV) estimation fusion theory, are developed for use in a centralized track-to-track fusion situation. The resulting global fusers can be implemented in a parallel structure to facilitate estimation fusion calculation. Simulation results show that the proposed SML estimator has a more robust capability of improving tracking accuracy than the CMM and the LMV estimators.

Distributed Kalman Filtering

“…Performance of various track-to-track fusion algorithms from aspects of fusion accuracy, feedback and process noises are treated in [263]. Perform track fusion optimally for a multiple-sensor system with a specific processing architecture is treated in [295]. Other work cited in Table 2.11 are [338], [15,22,23,24], [16], [17], [19], [21], [20], [71], [72], [73]- [74], [75]- [76], [77], [78]- [79], [124], [126], [260], [261,262], [264,265], [266], [296], [303], [305] and [306].…”

“…Other work cited in Table 2.11 are [338], [15,22,23,24], [16], [17], [19], [21], [20], [71], [72], [73]- [74], [75]- [76], [77], [78]- [79], [124], [126], [260], [261,262], [264,265], [266], [296], [303], [305] and [306]. [266] • Perform track fusion optimally for a multiple-sensor system with a specific processing architecture [295] • Track-to-track fusion for multi-sensor data fusion [296] • Common process noise on the two-sensor fused-track covariance [303] • Track association and track fusion with non-deterministic target dynamics [305] • Comparison of two-sensor tracking methods based on state vector fusion and measurement fusion [306] 2.9. DC-BASED ESTIMATION…”

Multi-sensor track fusion via Multiple-Model Adaptive Filter

“…Moreover, Chang et al. compared the performance of the algorithms described in [5], which was shown that IMF is optimal in the Minimum Mean Square Error (MMSE) sense and WCF method is only optimal in the Maximum Likelihood (ML) sense. Further more, Fong [7] demonstrated that IMF has better numerical robustness than WCF method by examining the case of using the mismatched common process noise.…”

“…Estimation fusion methods for solving the track-to-track fusion problem have been widely studied over three decades [1]- [9]. Currently, there are two typical state-vector fusion approaches: Weighted Covariance Fusion (WCF) [4], [5] and Information Matrix Filter (IMF) [6]. The IMF is information de-correlation method which was derived by using the information type of Kalman filter (KF).…”

Bayesian‐Based Domain Search Using Multiple Autonomous Vehicles with Intermittent Information Sharing

“…In [9] a probabilistic domain search and path planning problem is proposed for multiple uninhabited aerial vehicles (UAVs) under global communications. In [11] a distributed fusion scheme is proposed for multi-sensor surveillance. In [11] a distributed fusion scheme is proposed for multi-sensor surveillance.…”