An efficient multi-objective optimization approach for sensor management via multi-Bernoulli filtering

This article focuses on the efficient multi-objective particle swarm optimization algorithm to solve multidisciplinary design optimization problems. The objective is to extend the formulation of collaborative optimization which has been widely used to solve single-objective optimization problems. To examine the proposed structure, racecar design problem is taken as an example of application for three objective functions. In addition, a fuzzy decision maker is applied to select the best solution along the pareto front based on the defined criteria. The results are compared to the traditional optimization, and collaborative optimization formulations that do not use multi-objective particle swarm optimization. It is shown that the integration of multi-objective particle swarm optimization into collaborative optimization provides an efficient framework for design and analysis of hierarchical multidisciplinary design optimization problems.

This article introduces a novel consensus-based labeled multi-Bernoulli (LMB) filter to tackle multitarget tracking (MTT) in a distributed sensor network (DSN), whose sensor nodes have limited and different fields of view (FoVs). Although consensus-based algorithms are effective for distributed fusion and MTT, it may be problematic when distributed sensor nodes have different FoVs. To deal with this issue, the proposed method constructs an extended label space mapping to overcome the "label space mismatching" phenomenon; after that, the model of the undetected multitargets is established so that the tracks can be initialized outside the FoV of local sensors; finally and most important, weight selection and evolution mechanism are proposed such that the fusion weights are automatically tuned for each track at each time step and consensus step. The efficiency and robustness of the proposed algorithm are demonstrated in a distributed MTT scenario via numerical simulations.

This paper presents a novel methodology for realizing a computationally efficient multi-objective design optimization (CE-MODO) of switched reluctance motors (SRMs). Existing MODO approaches rely heavily on static and dynamic finite element analysis (FEA) to evaluate the electromagnetic performance parameters, which, despite their accuracy, incur significant computational costs. While analytical models reduce the dependence on static FEA, their limited accuracy constrains their applicability. Similarly, existing machine learning methods are inadequate for mapping multiple geometric parameters (GPs) to static characteristics across varied SRM designs. To overcome these limitations, this study proposes a novel integrated approach that combines K-means clustering with an artificial neural network (ANN) to enable accurate and efficient prediction of static characteristics. This technique led to a 52.16% decrease in the total computation time for determining the static characteristics of the SRM designs. Further, dynamic performance is evaluated using a MATLAB/Simulink-based SRM drive model, offering a computationally lightweight alternative to dynamic FEA. The proposed CE-MODO framework is applied to a four-phase 8/6 SRM topology designed for an electric three-wheeler, with average torque and electromagnetic losses as optimization objectives. Optimization is carried out by coupling the nondominated sorting genetic algorithm II (NSGA-II) with Kriging surrogate models, significantly reducing the computational load. The proposed methodology achieved an 11.01% improvement in average torque and a 4.56% reduction in electromagnetic losses compared to the initial design. The FEA models corresponding to both static and dynamic analyses employed in this study are further validated through experimental testing on a fabricated SRM prototype.

A surrogate-assisted multi-objective evolutionary algorithm with dimension-reduction for production optimization

Multi static radar systems offer advantages over mono static systems due to spatial diversity. Since each receiver can produce a detection, multiple detections are generated from a single target. Without a proper fusion strategy, this may lead to redundant tracks. Plot fusion, where detections are fused before tracking, enables the tracker to operate as in a mono static setting. While plot fusion has been explored for single target cases, prior works do not address the data association problem in multi target tracking. In this study, a new plot fusion scheme is proposed to associate detections from different receivers that originate from the same target. Time of Arrival and Time Difference of Arrival measurement models are analyzed both individually and in combination. The environment includes clutter, increasing the likelihood of false alarms and missed detections. A multi Bernoulli filter is used for tracking, and performance is evaluated using the Optimal Subpattern Assignment metric. Results show that Time of Arrival based models achieve more stable and accurate tracking.

In real world multiple extended target tracking problems, the presence of merged measurements is a frequently occurring phenomenon, however, most existing tracking algorithms in the literature assume that each target generates independent measurements. When this measurement merging phenomenon occurs, it increases the computational complexity of the tracking algorithms. Recently, the conditional joint decision and estimation (CJDE) algorithm based on the generalized Bayes risk was proposed to solve problems of joint detection, tracking and classification (JDTC) of targets. In this paper, we develop a principled Bayesian solution to the important problem involving inter-dependent decision and estimation conditioned on data based on the theory of random finite sets (RFS), and a tractable implementation based on the recently proposed generalized labeled multi-Bernoulli (GLMB) filter. The performance of the proposed technique is demonstrated by simulation of a multi-target bearings-only tracking scenario, where measurements become merged due to finite resolution effects.

Adaptively modeling the target birth intensity while maintaining the filtering efficiency is a challenging issue in multi-target tracking (MTT). Generally, the target birth probability is predefined as a constant and only the target birth density is considered in existing adaptive birth models, resulting in deteriorated target tracking accuracy, especially in the target appearing cases. In addition, the existing adaptive birth models also give rise to a decline in operation efficiency on account of the extra birth modeling calculations. To properly adapt the real variation of the number of newborn targets and improve the multi-target tracking performance, a novel fast sequential Monte Carlo (SMC) adaptive target birth intensity cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter is proposed in this paper. Through adaptively conducting the target birth probability in a pre-processing step, which incorporates the information of current measurements to correct the pre-setting of the target birth probability, the proposed filter can truly adapt target birth cases and achieve better tracking accuracy. Moreover, the implementation efficiency can be improved significantly by employing a measurement noise-based threshold in the likelihood calculations of the multi-target updating. Simulation results verify the effectiveness of the proposed filter.

This study proposes an efficient asset management framework that enables decision makers to prioritize the maintenance of their roads, while focusing on the most critical road segments. In particular, this study first extends the application of reliability theory to estimate the overall network condition. Following that, this study proposes a new consequence of failure function for the whole road network based on road segments’ reliability, age, and road agency preferences. Finally, the study proposes an efficient multi-objective optimization algorithm, with the goal of maximizing overall network performance with the least maintenance and computational cost. The suggested framework was applied to three main roads in Jordan and validated statistically by comparing its performance to that of a typical multi-objective genetic algorithm (GA) under various scenarios and utilizing multiple performance metrics. The results show that the proposed algorithm outperforms the traditional GA in terms of objective function values, convergence, and solution spread.

We propose a unified framework for sensor management in multi-modal sensor networks, which is inspired by the trading behavior of economic agents in commercial markets. Each sensor node (SN) acts as a seller who wants to sell the data it collects, to the sensor network manager (SM) who acts as a buyer. The resources and the data are priced by looking to balance global supply and demand, with the SN required to purchase resources for producing the data, and the SM required to purchase data to accomplish his tasks. We model this interaction as a double sided market, with both consumers and producers, and propose an iterative double auction mechanism for computing the equilibrium of such a market. We relate the equilibrium point to the solutions of sensor selection (SS), resource allocation (RA), and data fusion (DF) problems, which constitute the sensor management. The proposed framework will enable the system to determine the kind and the amount of data that should be produced, and to combine the data that is produced at each SN. To illustrate this framework, we consider the problem of multiple-target tracking as an example. Numerical examples demonstrate the effectiveness of the proposed method, and show that appropriate sensor management will result in an accurate estimate of the number of targets in the scene, higher correct identifications of the targets, and a lower mean-squared error in the estimates of their positions and velocities.

Multi-sensor systems are increasingly being deployed in many application scenarios due to the enormous potential they can offer. However, as the processing of sensory data often results in imprecise outcome, measuring the quality of information (QoI) in these systems has become an important issue. The measurement of QoI is usually performed by processing the elementary data provided by the heterogeneous sensors, which is also influenced by the techniques involved in sensor management. However, the effect of context, such as environmental geometry, sensor placement, orientation, time, and other parameters in computing QoI has not yet been explored extensively in the literature. This paper proposes a context evolution model and studies its impact in the QoI computation. In particular, we show that the dynamic context information can be utilized to manage a multi-sensor system to improve its QoI.

The Multi-Bernoulli filter propagates the multitarget posterior probability density forward under the precondition that targets are indistinguishable, whereas distinguishing target identities is important in real tracking systems. The recently proposed Labeled Multi-Bernoulli (LMB) filter addresses the target trajectories and track labels that makes it more suitable for practical applications. For tracking targets with bearing-only measurements, the classical nonlinear filters sometimes are not effective. In this paper, we propose an improved LMB filter which replaces the one Gaussian in the likelihood function with a Gaussian mixture. Simulation results show that the proposed method successfully reduces estimation error with tractable computational cost.

Most practical multi-objective optimization problems are often characterized by two or more expensive and conflicting objectives, which require time-consuming simulations. Commonly used algorithms construct a surrogate model of each objective function from a few high-fidelity solutions. In order to further decrease the computational burden, adaptive infilling strategies for multi-objective problems are developed to guide the next infilling design for expensive evaluation and update the surrogate model as well as the Pareto front in an iterative manner. In this paper, a multi-objective infilling strategy integrating the Kriging model with a two-stage infilling framework is proposed, termed as ATKIS. This method allows exploitation and exploration alternately to pinpoint the infilling solution for improving the Pareto set and avoiding local over-exploitation simultaneously. At the local exploitation stage, Kriging-based prediction and uncertainty estimation are combined with Non-dominant Ranking and Minimum Relative Distance theories for determining a new design solution, which has maximum improvement relative to the current Pareto set. At the global exploration stage, Voronoi tessellation theory is employed to search for the sparsest position in the design space for a new evaluation. The proposed method is compared with five recent infilling strategies to investigate the performance of infilling ability using several numerical benchmarks. The experimental results show that the proposed method outperforms the other three strategies in improving both effectiveness and robustness using the improvement of hypervolume as the evaluating indicator. In addition, a lightweight optimization design of hoist sheaves shows that the proposed method can deal with real engineering problems, while significantly reducing the computational time and the number of expensive simulations of samples.

Sensor selection is a very challenging issue where the choice of appropriate sensors should be made during the tracking processing. In order to improve the utilization of the wireless sensor networks, this paper presents an adaptive selection approach. The proposed method is dedicated to dynamically adjusting the number and the state of sensors involved. To avoid the lag of the sensor selection process, the partially observed Markov decision process (POMDP) is introduced. Since the multi-sensor is involved, we design an intuitive fusion process for the state estimation. Besides, the metric that maximizes the probability of detection is introduced to evaluate the optimal combination of sensors. A typical multi-target tracking scenario is studied in a wireless sensor networks. The results verify the effectiveness of algorithm in the selection of the sensors.

Tactical aerial reconnaissance missions using small unmanned aerial systems (UASs) have become a common scenario in the military. In particular, the detection of visually obscured objects such as camouflage materials and unexploded ordnance (UXO) is of great interest. Hyperspectral sensors, which provide detailed spectral information beyond the visible spectrum, are highly suitable for this type of reconnaissance mission. However, the additional spectral information places higher demands on system architectures to achieve efficient and robust data processing and object detection. To overcome these challenges, the concept of data reduction by band selection is introduced. In this paper, a specialized and robust concept of context-based hyperspectral sensor management with an implemented methodology of band selection for small and challenging UXO and camouflaged material detection is presented and evaluated with two hyperspectral datasets. For this purpose, several anomaly detectors such as LRX, NCC, HDBSCAN, and bandpass filters are introduced as part of the detection workflows and tested together with the sensor management in different system architectures. The results demonstrate how sensor management can significantly improve the detection performance for UXO compared to using all sensor bands or statistically selected bands. Furthermore, the implemented detection workflows and architectures yield strong performance results and improve the anomaly detection accuracy significantly compared to common approaches of processing hyperspectral images with a single, specialized anomaly detector.

Consider the problem of controlling a network of surveillance sensors that are capable of selecting which areas to observe and which modes to observe these areas. In this paper, we study the problem of controlling the observations of these sensors adaptively in order to classify accurately a collection of objects using information on their observed features. Our proposed approach is modeling objects as templates of 3-D features, and modeling sensors as observing features of individual objects, subject to degradation by noise, obscuration, missed detections and background clutter. We exploit a statistical framework based on random sets similar to those used in multi target tracking to model the statistical relationship between observed features and object types to compute information theoretic estimates of the probability of error in classification. We present a novel approach for computation of these distances between distributions of random sets using k-best assignment algorithms. These estimates are combined with real time information to generate predictions of the information value of individual measurements for sensor management. Using these predictions, we develop assignment algorithms to compute sensor management strategies to minimize this bound. The resulting sensor management algorithms are capable of solving problems involving a large numbers of objects in real-time. We show simulations of the resulting algorithms for classifying 3 dimensional objects from 2-dimensional noisy projections that illustrate how the algorithms select complementary views to overcome obscuration and provide accurate classification. Our results show that our information-based sensor management algorithms achieve comparable classification accuracy to adaptive simulation-based approaches that evaluate the value of information, while requiring nearly five orders of magnitude less computation.