Sensor placement of multistatic radar system by using genetic algorithm

In an increasing number of scientific and commercial mission scenarios at sea, it is required to simultaneously localize a group of underwater targets. The latter may include moored systems, autonomous vehicles, and even human divers. For reasons that have to do with the unavailability of Global Positioning System underwater, cost reduction, and simplicity of operation, there is currently a surge of interest in the development of range-based multiple target localization systems that rely on the computation of the distances between the targets and a number of sensor nodes deployed at the ocean surface, equipped with acoustic range measuring devices. In the case of a single target, there is a wealth of literature on the problem of optimal acoustic sensor placement to maximize the information available for target localization using trilateration methods. In the case of multiple targets, however, the literature is scarce. Motivated by these considerations, we address the problem of optimal sensor placement for multiple underwater target positioning. In this setup, we are naturally led to a multiple objective optimization problem, the solution of which allows for the analysis of the trade-offs involved in the localization of the targets simultaneously. To this end, we resort to tools from estimation theory and multi-objective optimization. For each target, the function to be minimized (by proper choice of the sensor configuration) is related to the determinant of the corresponding Fisher information matrix, which yields information on the minimum possible covariance of the error with which the position of the target can be estimated using any non-biased estimator. To deal with the fact that more than one target is involved, we exploit the concept of multiple objective Pareto-optimal solutions to characterize the best possible accuracy with which each of the targets can be positioned, given constraints on the desired positioning accuracy of the other targets. Simulation examples illustrate how, for a three-sensor network and two targets, it is possible to define an optimal sensor configuration that yields large positioning accuracy for both targets simultaneously, using convex optimization tools. When more than two targets are involved, however, more than three sensors are required to exploit an adequate trade-off of the accuracy with which each target can be positioned by resorting to non-convex and Pareto-optimization tools. We show how in this case the optimal sensor configurations depend on the Pareto weights assigned to each of the targets, as well as on the number of sensors, the number of targets, and the uncertainty with which the positions of the targets are known a priori.

Accurate localization is a critical task in underwater navigation. Typical localization methods use a set of acoustic sensors and beacons to estimate relative position, whose geometric configuration has a significant impact on the localization accuracy. Although there is much effort in the literature to define optimal 2D or 3D sensor placement, the optimal sensor placement in irregular and constrained 3D surfaces, such as autonomous underwater vehicles (AUVs) or other structures, is not exploited for improving localization. Additionally, most applications using AUVs employ commercial acoustic modems or compact arrays, therefore the optimization of the placement of spatially independent sensors is not a considered issue. This article tackles acoustic sensor placement optimization in irregular and constrained 3D surfaces, for inverted ultra-short baseline (USBL) approaches, to improve localization accuracy. The implemented multi-objective memetic algorithm combines an evaluation of the geometric sensor's configuration, using the Cramer-Rao Lower Bound (CRLB), with the incidence angle of the received signal. A case study is presented over a simulated homing and docking scenario to demonstrate the proposed optimization algorithm.

New formulation and optimization methods for water sensor placement

We study the problem of actuator and sensor placement in a linear advection partial differential equation (PDE). The problem is motivated by its application to actuator and sensor placement in building systems for the control and detection of a scalar quantity such as temperature and contaminants. We propose a gramian based approach to the problem of actuator and sensor placement. The special structure of the advection PDE is exploited to provide an explicit formula for the controllability and observability gramian in the form of a multiplication operator. The explicit formula for the gramian, as a function of actuator and sensor location, is used to provide test criteria for the suitability of a given sensor and actuator location. Furthermore, the solution obtained using gramian based criteria is interpreted in terms of the flow of the advective vector field. In particular, the almost everywhere uniform stability property and ergodic properties of the advective vector field are shown to play a crucial role in deciding the location of actuators and sensors. Simulation results are performed to support the main results of this paper.

The optimal placement method of sensors is carried out based on improved genetic algorithm for solving the sensors optimal placement problem of the health monitoring system for long-span railway bridge. Dual structure coding method is introduced to improve the individual encoding method in the Genetic Algorithm. Adaptive partial matching crossover and inversus mutation method is adopted in the optimal preservation strategy, and the probabilities of crossover and mutation are changed automatically according to the fitness value for obtaining the global optimal solution of the sensor placement. So some defects in other Genetic Algorithm applied in the optimal placement of sensors for major bridge structure, such as slow convergence speed and easily falling into local optimum etc., are overcome, and the convergence is ensured. Then the optimal sensors placement of the health monitoring system for one certain long-span railway steel truss cable-stayed bridge is taken as the example to verify the proposed improved genetic algorithm. The result shows that the proposed method has better global optimization, computational efficiency and reliability in compare with the Simple Genetic Algorithm and General Genetic Algorithm, and can be applied to the actual railway cable-stayed bridge health monitoring system for the optimal sensors placement.

Underwater acoustic localization is a challenging task. Most techniques rely on a network of acoustic sensors and beacons to estimate relative position, therefore localization uncertainty becomes highly dependent on the selected sensor configuration. Although several works in literature exploit optimal sensor placement to improve localization over large regions, the conditions contemplated in these are not applicable for the optimization of the acoustic sensors on constrained 3D shapes, such as the body of small underwater vehicles or structures. Additionally, most commercial systems used for localization with ultra-short baseline (USBL) configurations have compact acoustic sensors that cannot be spatially positioned independently. This work tackles the optimization of acoustic sensor placement in a limited 3D shape, in order to improve the localization accuracy for USBL applications. The implemented multi-objective memetic algorithm combines the Cramer-Rao Lower Bound (CRLB) configuration evaluation with incidence angle considerations for the sensor placement.

Wastewater surveillance has received significant attention in recent years due to the valuable environmental and epidemiological information it reveals. Yet, limited attempts have been made in the previous literature to determine the best locations for sample collection. In this study, we are introducing a model to solve the sensor placement (SP) problem that considers source identification (SI) as the main criterion for sensor placement. The objective of this study is to maximize the ability of the sensor design to identify the characteristics of the injection source (i.e., the injection location and concentration) of any given species of interest. To that end, we developed a python-based model that couples an SI model with a greedy algorithm to determine the best locations to place water quality sensors. The developed SP model was demonstrated on a benchmark, mid-sized sewer network. The study results revealed that a clear tradeoff exists between the overall identifiability and the detection reliability of the sensor design. In general, placing the sensors in the downstream end of the network resulted in higher overall identifiability, while better detection reliability was achieved by the sensors placed in the upstream section of the network.

A dynamic monitoring of thermal behavior of hardware resources using thermal sensors is very important to maintain the operation of systems safe and reliable. This work proposes an effective solution to the problem of thermal sensor allocation and placement for reconfigurable systems at the post-manufacturing stage. Specifically, we define the sensor allocation and placement problem (SAPP), and propose a solution which formulates SAPP into the unate-covering problem (UCP) and solves it optimally. We then provide an extended solution to handle a practical design issue where the hardware resources for the sensor implementation on specific array locations have already been used up by the application logic. Experimental results using MCNC benchmarks show that our proposed technique uses 19.7% less number of sensors to monitor hotspots on the average than that used by the bisection based [1] approaches.

Optimal sensor placement is an important yet unsolved problem in control theory. In biological organisms, genetic activity is often highly nonlinear, making it difficult to design libraries of promoters to act as reporters of the cell state. We make use of the Koopman observability gramian to develop an algorithm for optimal sensor (or reporter) placement for discrete time nonlinear dynamical systems to ease the difficulty of design of the promoter library. This ease is enabled due to the fact that the Koopman operator represents the evolution of a nonlinear system linearly by lifting the states to an infinite-dimensional space of observables. The Koopman framework ideally demands high temporal resolution, but data in biology are often sampled sparsely in time. Therefore we compute what we call the temporally fine-grained Koopman operator from the temporally coarse-grained Koopman operator, the latter of which is identified from the sparse data. The optimal placement of sensors then corresponds to maximizing the observability of the fine-grained system. We demonstrate the algorithm on a simulation example of a circadian oscillator.

The mechanical design of wind turbines requires a profound understanding of the dynamic behaviour. Even though highly detailed simulation models are already in use to support wind turbine design, modal testing on a real prototype is irreplaceable to identify site-specific conditions such as the stiffness of the tower foundation. Correct identification of the mode shapes of a complex mechanical structure much depends on the placement of the sensors. For operational modal analysis of a 3 MW wind turbine with a 120 m rotor on a 100 m tower developed by W2E Wind to Energy, algorithms for optimal placement of acceleration sensors are applied. The mode shapes used for the optimisation are calculated by means of a detailed flexible multibody model of the wind turbine. Among the three algorithms in this study, the genetic algorithm with weighted off-diagonal criterion yields the sensor configuration with the highest quality. The ongoing measurements on the prototype will be the basis for the development of optimised wind turbine designs.

This paper addresses the problem of reliable sensor placement in large scale linear systems with potentially faulty components. The failure probabilities of individual system components are assumed to be known, and the goal is to place sensor outputs so that the network remains observable with high probability. Two different kinds of system component failures are considered in this paper: 1) the failure of arbitrary sensor devices and 2) the failure of arbitrary connections between pairs of state variables (referred to as a link). In addition, we focus on the design from an economic point of view; thus, we aim to identify the minimum number of state variables that need to be measured to meet desired reliability criteria. We recast this problem as an integer program. Although the integer programming problem is known to be NP-complete, we propose a greedy algorithm and characterize its performance with respect to the optimal solution. Consequently, the proposed approach provides an approximate solution to the problem of minimal sensor placement in the presence of stochastic component failure. Finally, we illustrate the obtained results with an example and simulation analysis.

In the control of distributed parameter systems, the problem of effective actuator and sensor placement is not well understood, and the use of suboptimal placement methodology is common. We discuss a technique we recently proposed that iterates toward the optimal actuator and sensor placement for ℋ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> synthesis, minimizing the closed-loop ℋ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> norm from exogenous inputs to exogenous outputs. The iteration is based on an efficient computation of the ℋ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> norm's gradient with respect to actuator and sensor placements.We demonstrate the technique on the Orr-Sommerfeld/Squire model of fluid flow, and review previous results from the linearized Ginzburg-Landau model. Finally, we use these results to analyze the validity of previous placement approaches.

Optimal resilient sensor placement problem for secure state estimation

Blasts can produce, in a very short time, an overload much greater than the design load of a building. The blast explosion nearby or within structures causes catastrophic damage to the building both externally and internally. This study intends to model a Cold-Formed Steel (CFS) building using Finite Element Method (FEM) in which material properties of the model are defined according to results of performed laboratory tests. Then accelerograph record of a standard blast was applied to the Finite Element (FE) model. Furthermore, various Optimal Sensor Placement (OSP) algorithms were used and Genetic Algorithm (GA) was selected to act as the solution of the optimization formulation in selection of the best sensor placement according to the blast loading response of the system. In this research a novel numerical algorithm was proposed for OSP procedure which utilizes the exact value of the structural response under blast excitation. Results show that with a proper OSP method for Structural Health Monitoring (SHM) can detect the weak points of CFS structures in different parts efficiently.

A Deep Reinforcement Learning Approach to Sensor Placement under Uncertainty