Optimal Subpattern Assignment Distance Research Articles

Robust adaptive multi‐target tracking with unknown measurement and process noise covariance matrices

A robust adaptive probability hypothesis density (PHD) filter is proposed to address the degradation of PHD performance due to an unknown process noise and measurement noise covariance matrix. Therefore, the inverse Wishart distribution is introduced to model the prior distribution of process noise and measurement noise. Meanwhile, the multi-target posterior intensity is approximated as a mixture of the inverse Wishart distribution and Gaussian distribution. The closed solution of the robust PHD filter is derived by the variational Bayes approach. Simulation results show that the proposed algorithm outperforms the Gaussian-mixed PHD filter and the variational Bayesian PHD filter in terms of target number estimation accuracy and optimal sub-pattern assignment distance.

Multiple Radio Transmitter Localization via UAV-Based Mapping

The widespread use of unmanned aerial vehicles (UAVs) has opened a novel perspective on spectrum awareness and localization because of their ability to overcome spatial limits and the burden of dedicated infrastructure. In such scenarios, an underinvestigated problem is the simultaneous localization of multiple non-collaborative primary users (PUs) of the spectrum, whose number, transmit power, activity pattern, and signal structure are unknown. This work proposes a framework for multiple PU localization based on the received power measured by an antenna array mounted on a UAV. A score map is firstly constructed based on the measured power. Then two algorithms, k-means clustering and weighted centroid (KCWC), and Gaussian mixture model fitting (GMMF) are applied to the score map to estimate the number and the positions of the PUs. The performance is evaluated in terms of optimal subpattern assignment (OSPA) distance, compared with a genie-aided (GA) localization algorithm capable of separating the signals emitted by the PUs. Despite the blind nature of the method proposed, numerical results exhibit very good localization accuracy, with an OSPA distance below <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$6\,$</tex-math></inline-formula> m in large, line-of-sight (LOS)-dominated, outdoor scenarios with five PUs, even in the presence of channel impairments and UAV position and heading uncertainties.

New Accuracy Evaluation Index for Track Fusion Algorithms

When evaluating the track fusion algorithm, common accuracy indexes may fail to evaluate the fusion accuracy correctly when the state estimation and the real target cannot be one-to-one, and fail to effectively distinguish the performance of the algorithm when the state estimation is similar. Therefore, it is necessary to construct a high-resolution evaluation index, which can evaluate the track fusion algorithm more accurately, reasonably and comprehensively. Firstly, the advantages and disadvantages of the optimal subpattern assignment (OSPA) distance as the accuracy index to evaluate the track fusion algorithm are analyzed. Then, its deficiencies are improved by using the Hellinger distance instead of the original Euclidean distance, and the distance is index transformed. Finally, a new evaluation index for track fusion algorithms is proposed, which is the OSPA distance based on Hellinger distance and index transformation. The simulation results show that the new index can not only correctly evaluate the fusion precision, but also consider the state uncertainty, making that can evaluate the track fusion algorithm more sensitively, and effectively solves the sensitivity of the index to the cut-off parameter c through index transformation.

Substructural damage detection in shear structures via ARMAX model and optimal subpattern assignment distance

A novel substructural damage detection approach based on auto-regressive moving average with exogenous inputs (ARMAX) model and optimal subpattern assignment (OSPA) distance is proposed to locate and quantify the damages. Firstly, the shear structure is divided into independent substructures so that damage identification can be performed on each substructure modeling with multi-input multi-output (MIMO) model, which is promising for practical application and distributed structural health monitoring. Compared with the auto-regressive (AR) model, the ARMAX model structure involves not only the outputs but also the inputs and the disturbance dynamics, which help to improve the performance of modeling every substructure and gain the flexibility to handle the disturbance caused by environmental noises even the structural responses contain strong correlations under different excitations. The features of a linear dynamic system can be represented by its poles estimated from ARMAX model, transforming the damage detection into multi-target tracking of system poles. The OSPA distance is used as an innovative damage indicator of a function of the ARMAX model poles in this study, and is calculated by Hungarian algorithm. Experimental verifications were conducted to prove satisfactory damage detection. The application on nonlinear damage identification in a complex three-dimensional reinforced concrete structure shows the great potential of the proposed pole-based OSPA distance in multi-sensor information fusion of structural responses from different directions.

Error bounds for joint detection and estimation of multiple unresolved target-groups

According to random finite set (RFS) and information inequality, this paper derives an error bound for joint detection and estimation (JDE) of multiple unresolved target-groups in the presence of clutters and missed detections. The JDE here refers to determining the number of unresolved target-groups and estimating their states. In order to obtain the results of this paper, the states of the unresolved target-groups are modeled as a multi-Bernoulli RFS first. The point cluster model proposed by Mahler is used to describe the observation likelihood of each group. Then, the error metric between the true and estimated state sets of the groups is defined by the optimal sub-pattern assignment distance rather than the usual Euclidean distance. The maximum a posteriori detection and unbiased estimation criteria are used in deriving the bound. Finally, we discuss some special cases of the bound when the number of unresolved target-groups is known a priori or is at most one. Example 1 shows the variation of the bound with respect to the probability of detection and clutter density. Example 2 verifies the effectiveness of the bound by indicating the performance limitations of the cardinalized probability hypothesis density and cardinality balanced multi-target multi-Bernoulli filters for unresolved target-groups. Example 3 compares the bound of this paper with the (single-sensor) bound of [4] for the case of JDE of a single unresolved target-group. At present, this paper only addresses the static JDE problem of multiple unresolved target-groups. Our future work will study the recursive extension of the bound proposed in this paper to the filtering problems by considering the group state evolutions.