Intelligence-Aware Batch Processing for TMA with Bearings-Only Measurements.

Gabriele Oliva,Alfonso Farina,Roberto Setola

doi:10.3390/s21217211

Gabriele Oliva, Alfonso Farina + Show 1 more

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https://doi.org/10.3390/s21217211

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Abstract

This paper develops a framework to track the trajectory of a target in 2D by considering a moving ownship able to measure bearing measurements. Notably, the framework allows one to incorporate additional information (e.g., obtained via intelligence) such as knowledge on the fact the target’s trajectory is contained in the intersection of some sets or the fact it lies outside the union of other sets. The approach is formally characterized by providing a constrained maximum likelihood estimation (MLE) formulation and by extending the definition of the Cramér–Rao lower bound (CRLB) matrix to the case of MLE problems with inequality constraints, relying on the concept of generalized Jacobian matrix. Moreover, based on the additional information, the ownship motion is chosen by mimicking the Artificial Potential Fields technique that is typically used by mobile robots to aim at a goal (in this case, the region where the target is assumed to be) while avoiding obstacles (i.e., the region that is assumed not to intersect the target’s trajectory). In order to show the effectiveness of the proposed approach, the paper is complemented by a simulation campaign where the MLE computations are carried out via an evolutionary ant colony optimization software, namely, mixed-integer distributed ant colony optimization solver (MIDACO-SOLVER). As a result, the proposed framework exhibits remarkably better performance, and in particular, we observe that the solution is less likely to remain stuck in unsatisfactory local minima during the MLE computation.

Highlights

In the last decades, target motion analysis (TMA) has become an increasingly popular research field, and in the literature, several approaches have been developed, such as batch processing frameworks [1,2,3,4] and recursive ones [3,5,6,7,8]
We develop a novel maximum likelihood estimation (MLE) approach to carry out batch target-tracking estimation based on noisy bearing-only measurements, which incorporates as inequality constraints additional information in terms of sets where the target’s trajectory is assumed to be contained and other sets which have empty intersection with the target’s trajectory; We characterize the Cramér–Rao lower bound (CRLB) associated to the constrained problem by considering a generalized set-valued Jacobian matrix of the constraints function and by resorting to nonsmooth theory; Sensors 2021, 21, 7211
This paper presents a batch strategy to estimate the parameters describing the trajectory of a target, based on a moving ownship able to measure bearings

Summary

Introduction

Target motion analysis (TMA) has become an increasingly popular research field, and in the literature, several approaches have been developed, such as batch processing frameworks [1,2,3,4] and recursive ones [3,5,6,7,8]. The aim of TMA is to estimate the state of a target (usually position and velocity) from noise-corrupted measurements collected by an observer [9]. The TMA problem presents several challenges, mainly due to the nonlinear relationship between the measurements and target state. Another challenge is that the observer must outmaneuver the target in order to make the target state observable [10]. To track a target with constant velocity, the observer platform must change its speed or course. There exist other target trajectories that produce the same sequence of noise-free bearing angles [3]

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