Abstract
Successful track-to-track association (TTTA) in a multisensor and multitarget scenario is predicated on a reasonable likelihood function to evaluate the similarity of asynchronous mono tracks. To deal with the lack of synchronous data and prior knowledge of the targets in practical applications, this paper investigates a global optimization method with a novel likelihood function constructed by finite asynchronous measurements with joint temporal and spatial constraints (JTSC). For a scenario with more than two independent sensors, a sequential two-stage strategy is proposed to calculate the similarity of multiple asynchronous mono tracks. For the first stage, based on the temporal features of measurements from different sensors, a pairwise fusion model to estimate the position of the target with two mono tracks is established based on the asynchronous crossing location approach. For the other stage, to evaluate the similarity of the outputs, a pairwise similarity model is constructed by searching for the optimal matching points by setting temporal and spatial constraints. Thus, the likelihood of multiple asynchronous tracks is obtained. Simulations are performed to verify that the proposed method can achieve favorable performance without data-synchronization, and also demonstrate the superiority over the methods based on hinge angle differences (HADs) in some scenarios.
Highlights
Unmanned aerial vehicles (UAVs) flying in formation are becoming more popular due to the increasing demand of cooperative tasks [1,2]
A novel likelihood is proposed by an association strategy to couple multiple asynchronous mono of the proposed joint temporal and spatial constraints (JTSC) method over the existing methods based on hinge angle differences (HADs)
Motivated by the idea of track-to-track association (TTTA) methods compared with the reference topologies in radar systems, this section presents the three key components of the tuple likelihood for asynchronous mono tracks, listed as: (a) the sequential two-stage fusion-based strategy; (b) the pairwise fusion model; and (c) the pairwise similarity model
Summary
Unmanned aerial vehicles (UAVs) flying in formation are becoming more popular due to the increasing demand of cooperative tasks [1,2]. This paper proposes a novel likelihood function containing a sequential two-stage fusion-based strategy with pairwise mono track fusion and pairwise stereo track similarity evaluation, which can directly deal with the asynchronous measurements. A novel likelihood is proposed by an association strategy to couple multiple asynchronous mono of the proposed JTSC method over the existing methods based on HADs. tracks and evaluate their similarity in a unified sequential framework without simultaneous measurements. A pairwise fusion model the using two mono asynchronous to estimate thein potential stereo track of a hypothesis test, providing global likelihood and the notation used this paper. This paper, focuses on the formulation and analysis of suitable approximations of the tuple likelihood, i.e., l(T ),
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