Threshold Sensor Research Articles

Modeling for a System Operating Characteristic for Wide Area Mines

Traditional landmines are limited in their attack range and information providing ability. There are currently several wide area search mines in the research and development phase to overcome these disadvantages. Progress on the individual technologies is promising, but there are insufficient analytical tools for evaluating the effectiveness of these concept mines. This paper examines some of the modeling aspects of wide area search mines with Autonomous Target Recognition (ATR) capability. The optimal employment of autonomous wide area search mines is addressed. The specific scenarios considered involve several mines searching a battle fields for targets in the presence of false targets. All relevant parameters are extracted from intelligence information, the sensor performance specification, and the vehicle performance specification. Analytic system effectiveness measures are derived using applied probability theory. Optimal schedules for controlling sensor threshold during a mission are derived. These schedules establish a system operating characteristic. An increase in system effectiveness is demonstrated when parameters are dynamically controlled during a mission.

Noise Mitigation for Target Tracking in Wireless Acoustic Sensor Networks

In wireless sensor network (WSN) environments, environmental noises are generated by, for example, small passing animals, crickets chirping or foliage blowing and will interfere target detection if the noises are higher than the sensor threshold value. For accurate tracking by acoustic WSNs, these environmental noises should be filtered out before initiating track. This paper presents the effect of environmental noises on target tracking and proposes a new algorithm for the noise mitigation in acoustic WSNs. We find that our noise mitigation algorithm works well even for targets with sensing range shorter than the sensor separation as well as with longer sensing ranges. It is also found that noise duration at each sensor affects the performance of the algorithm. A detection algorithm is also presented to account for the Doppler effect which is an important consideration for tracking higher-speed ground targets. For tracking, we use the weighted sensor position centroid to represent the target position measurement and use the Kalman filter (KF) for tracking.

Input design in worst-case system identification with quantized measurements

This paper addresses the problem of set membership system identification with quantized measurements. Following the work developed for binary measurements, the problem of optimal input design with multiple sensor thresholds is tackled. For a FIR model of order n, the problem is decomposed into n static gain problems. The one-step optimal input problem is solved both for equispaced and generic sensor threshold distribution. Moreover, the N-step optimal input problem for the case of equispaced thresholds is addressed, and a solution is provided under a suitable assumption on the sensor range and resolution. The obtained results allow us to construct an upper bound on the time complexity of the FIR identification problem for the case of equispaced thresholds. Numerical application examples are reported to show the effectiveness of the proposed algorithms.

Risk minimization in biometric sensor networks: an evolutionary multi-objective optimization approach

Biometric systems aim at identifying humans by their characteristics or traits. This article addresses the problem of designing a biometric sensor management unit by optimizing the risk, which is modeled as a multi-objective optimization (MO) problem with global false acceptance rate and global false rejection rate as the two objectives. In practice, when multiple biometric sensors are used, the decision is taken locally at each sensor and the data are passed to the sensor manager. At the sensor manager, the data are fused using a fusion rule and the final decision is taken. The optimization process involves designing the data fusion rule and setting of the sensor thresholds. In this work, we employ a fuzzy dominance and decomposition-based multi-objective evolutionary algorithm (MOEA) called MOEA/DFD and compare its performance with two state-of-the-art MO algorithms: MOEA/D and NSGA-II in context to the risk minimization task. The algorithm introduces a fuzzy Pareto dominance concept to compare two solutions and uses the scalar decomposition method only when one of the solutions fails to dominate the other in terms of a fuzzy dominance level. The MO algorithms are simulated on different number of sensor setups consisting of three, six, and eight sensors. The a priori probability of imposter is also varied from 0.1 to 0.9 to verify the performance of the system with varying degrees of threat. One of the most significant advantages of using the MO framework is that with a single run, just by changing the decision-making logic applied to the obtained Pareto front, one can find the required threshold and decision strategies for varying threats of imposter. However, with single-objective optimization, one needs to run the algorithms each time with change in the threat of imposter. Thus, multi-objective formulation of the problem appears to be more useful and better than the single-objective one. In all the test instances, MOEA/DFD performs better than all the other algorithms.

External Sensors for Detecting the Activation and Deactivation Times of the Major Muscles Used in Walking

Functional electrical stimulation (FES) can improve walking in individuals with mobility impairments. We evaluated accelerometers, force sensitive resistors, segment angles, and segment angular velocities to identify which sensor best determines the activation and deactivation times of the main muscles used during walking. This sensor(s) can be used in the future in conjunction with FES systems to improve walking. Able-bodied subjects walked at various speeds. Threshold levels were set for each sensor that minimized the difference between the times of activating and deactivating the electromyogram (EMG) of six muscles and the times of sensor threshold crossings as a percent of the step cycle. Mobility-impaired subjects walked at their preferred speed with and without FES to correct foot drop. Thresholds were set for these subjects so that sensor signals would cross at times that matched those of able-bodied subjects. Segment angles were generally the most effective sensor signals. Using segment angles of the thigh, shank, and foot, activation and deactivation times of the six muscles could be determined to within 6% of the step cycle. The shank segment angle produced the lowest overall error and was among the top three sensors for 10 of the 12 events (activation and deactivation of six muscle groups). A segment angle sensor was implemented using a complementary filter (accelerometer/gyroscope combination). Using this sensor improved rule-based timing of FES in subjects with foot drop as compared to accelerometers alone.

Impact of sensor characteristics on source characterization for dispersion modeling

An accidental or intentional release of hazardous chemical, biological, radiological, or nuclear material into the atmosphere obligates responsible agencies to model its transport and dispersion in order to mitigate the effects. This modeling requires input parameters that may not be known and must therefore be estimated from sensor measurements of the resulting concentration field. The genetic algorithm (GA) method used here has been successful at back-calculating not only these source characteristics but also the meteorological parameters necessary to predict the contaminants subsequent transport and dispersion. This study assesses the impact of sensor thresholds, i.e. the sensor minimum detection limit and saturation level, on the ability of the algorithm to back-calculate modeling variables. The sensitivity of the back-calculation to these sensor constraints is analyzed in the context of an identical twin approach, where the data is simulated using the same Gaussian Puff model that is used in the back-calculation algorithm in order to analyze sensitivity in a controlled environment. The solution is optimized by the GA and further tuned with the Nelder–Mead downhill simplex algorithm. For this back-calculation to be successful, it is important that the sensor capture the maximum concentrations.

Optimization of container inspection strategy via a genetic algorithm

It is estimated that 90% of the world’s freight is moved as containerized cargo, with over 125 million TEUs (Twenty foot Equivalent Units) of container being shipped by 2010. To inspect this volume of cargo for explosives, drugs or other contraband is a daunting challenge. This paper presents an optimization technique for developing an inspection strategy that will provide a specified detection rate for containers containing contraband at a minimum cost. Nested genetic algorithms are employed to optimize the topology of an inspection strategy decision tree, the placement of sensors on the tree and the sensor thresholds which partition suspicious containers (containers believed to contain contraband) from innocuous containers (containers which are believed to be free of contraband). The results of this optimization technique are compared to previously published techniques.

Optimization of an Autonomous Weapon System's Operating Characteristic

Optimal employment of autonomous wide area search munitions or unmanned aerial vehicles is considered. The air vehicle searches a battle space for stationary targets in the presence of false targets and/or clutter. Control problems are formulated to achieve optimal scheduling of the control variables P TR, the probability of target report, which is a reflection of the sensor's threshold setting, and Q, the area coverage rate. These optimal control problem formulations yield solutions that maximize the probability of target attack, and constraints to also satisfy hard bounds on the probability of false target attacks may also be applied. Analysis has shown that the optimal solutions are sensitive to the particular relationship between the autonomous target recognition module's receiver operating characteristic parameter, c , and area coverage rate, Q . In this paper the relationship is developed from first principles for both passive and active sensors, and the optimal P TR and Q schedules are obtained.

Biometric Sensor Management: Tradeoffs in Time, Accuracy and Energy

In this paper a novel sensor management algorithm is presented for a biometric sensor network. A distributed detection framework is managed for different energy, accuracy and time requirements. The design variables include sensor thresholds, fusion rule, sensor selection and sensor mode selection. Different sensors are associated with different transaction times. Hence, varying sensor modes can affect the accuracy and energy consumption. Once the sensors and their modes are selected, the accuracy achieved by this subset of sensors is maximized by managing the thresholds and the fusion rule. Risk, time and energy are the three objectives that the system attempts to minimize. The three objectives are tied into a single objective function by weighting them. A hybrid particle swarm optimization algorithm is design the system. The algorithm is a hybrid of continuous, discrete and binary particle swarm. The continuous particle swarm is used to manage the thresholds. The binary particle swarm is used to manage the fusion rule. The discrete particle swarm is used to select the sensors and the sensor mode. The system is adapted for different threat levels that depend on the a priori of imposter in the network. Results show the effectiveness of the proposed method in adapting the system to different requirements under different threat situations.

An evolutionary algorithm for port-of-entry security optimization considering sensor thresholds

According to the US Customs and Border Protection (CBP), the number of offloaded ship cargo containers arriving at US seaports each year amounts to more than 11 million. The costs of locating an undetonated terrorist weapon at one US port, or even worst, the cost caused by a detonated weapon of mass destruction, would amount to billions of dollars. These costs do not yet account for the devastating consequences that it would cause in the ability to keep the supply chain operating and the sociological and psychological effects. As such, this paper is concerned with developing a container inspection strategy that minimizes the total cost of inspection while maintaining a user specified detection rate for “suspicious” containers. In this respect and based on a general decision-tree model, this paper presents a holistic evolutionary algorithm for finding the following: (1) optimal threshold values for every sensor and (2) the optimal configuration of the inspection strategy. The algorithm is under the assumption that different sensors with different reliability and cost characteristics can be used. Testing and experimentation show the proposed approach consistently finds high quality solutions in a reduced computational time.

A Multiobjective Optimization Approach to Obtain Decision Thresholds for Distributed Detection in Wireless Sensor Networks

For distributed detection in a wireless sensor network, sensors arrive at decisions about a specific event that are then sent to a central fusion center that makes global inference about the event. For such systems, the determination of the decision thresholds for local sensors is an essential task. In this paper, we study the distributed detection problem and evaluate the sensor thresholds by formulating and solving a multiobjective optimization problem, where the objectives are to minimize the probability of error and the total energy consumption of the network. The problem is investigated and solved for two types of fusion schemes: 1) parallel decision fusion and 2) serial decision fusion. The Pareto optimal solutions are obtained using two different multiobjective optimization techniques. The normal boundary intersection (NBI) method converts the multiobjective problem into a number of single objective-constrained subproblems, where each subproblem can be solved with appropriate optimization methods and nondominating sorting genetic algorithm-II (NSGA-II), which is a multiobjective evolutionary algorithm. In our simulations, NBI yielded better and evenly distributed Pareto optimal solutions in a shorter time as compared with NSGA-II. The simulation results show that, instead of only minimizing the probability of error, multiobjective optimization provides a number of design alternatives, which achieve significant energy savings at the cost of slightly increasing the best achievable decision error probability. The simulation results also show that the parallel fusion model achieves better error probability, but the serial fusion model is more efficient in terms of energy consumption.

Smart Irrigation Controllers: How Do Soil Moisture Sensor (SMS) Irrigation Controllers Work?

Revised! AE437, a 5-page illustrated fact sheet by Michael D. Dukes, Mary Shedd, and Bernard Cardenas-Lailhacar, is part of the Smart Irrigation Controllers series. It describes how soil moisture sensor systems work, rules for installation, setting the sensor threshold, and programming the irrigation timer. Includes references. Published by the UF Department of Agricultural and Biological Engineering, March 2009. AE437/AE437: Smart Irrigation Controllers: How Do Soil Moisture Sensor (SMS) Irrigation Controllers Work? (ufl.edu)

Port-of-Entry Inspection: Sensor Deployment Policy Optimization

This paper considers the problem of container inspection at a port-of-entry. Containers are inspected through a specific sequence to detect the presence of nuclear materials, biological and chemical agents, and other illegal shipments. The threshold levels of sensors at inspection stations of the port-of-entry affect the probabilities of incorrectly accepting or rejecting a container. In this paper, we present several optimization approaches for determining the optimum sensor threshold levels, while considering misclassification errors, total cost of inspection, and budget constraints. In contrast to previous work which determines threshold levels and sequence separately, we consider an integrated system and determine them simultaneously. Examples applying the approaches in different sensor arrangements are demonstrated.

State Observers and Stabilization of Systems with Binary-Valued Observations

This paper introduces an observer for linear time invariant systems in continuous time whose outputs are measured only by binary-valued sensors. The traditional observers will fail in general, even if the system has a full-rank observability matrix. It is shown that by controlling the sensor threshold, it is always possible to cause the output to cross the sensor threshold within a designated time interval. Information on the time instants of threshold crossing is then used to develop state estimates. Convergence properties of the state observer are established. It is shown that by generating appropriate switching time sequences, state observers are convergent in the mean squares sense, with probability one, and exponentially. Combined with a state feedback using the state estimates, this state estimator leads to a stabilizing output feedback.

Space and time complexities and sensor threshold selection in quantized identification

This work is concerned with system identification of plants using quantized output observations. We focus on relationships between identification space and time complexities. This problem is of importance for system identification in which data-flow rates are limited due to computer networking, communications, wireless channels, etc. Asymptotic efficiency of empirical measure based algorithms yields a tight lower bound on identification accuracy. This bound is employed to derive a separation principle of space and time complexities and to study sensor threshold selection. Insights gained from these understandings provide a feasible approach for optimal utility of communication bandwidth resources in enhancing identification accuracy.

World-wide lightning location using VLF propagation in the Earth-ionosphere waveguide

Worldwide lightning location (WWLL) using only 30 lightning sensors has been successfully achieved by using only VLF propagation in the Earth-ionosphere waveguide (EIWG). Ground propagation or mixed "sky" and ground propagation is avoided by requiring evidence of Earth-ionosphere waveguide dispersion. A further requirement is that the lightning strike must be inside the perimeter defined by the lightning sensor sites detecting the stroke. Under these conditions, the time and the location of the stroke can be determined, along with the rms errors. Lightning strokes with errors exceeding 30 Ps or To assist with identifying impulses from the same lightning stroke, the lightning sensor threshold is automatically adjusted to allow an average detection rate of three per second. This largely limits detection to the strongest 4% of all lightning strokes, of which about 40% meet the accuracy requirements for time and location.

State reconstruction for linear time-invariant systems with binary-valued output observations

This paper focuses on state reconstruction problems for systems whose outputs are measured only by binary-valued sensors. A binary-valued sensor is characterized by its switching threshold or post-switching location and by its output that indicates only whether the system output is above or below the threshold. The traditional passive-type state reconstruction that does not require input design will fail in general, even if the system has a full-rank observability matrix. It is shown that under controllability conditions and bounded uncertainty on the initial state, it is always possible to construct a causal input that will cause the output to cross the sensor threshold in a designated time interval. Information on the time instants of threshold crossing is then used to reconstruct the initial state. It is proved that if the system is observable, such a reconstruction is always possible if the eigenvalues of the system are all real valued. If the eigenvalues of the system contain only purely imaginary and non-repeating values, it is sufficient that threshold crossing occurs within a relatively small time interval. In general without constraints on system eigenvalues, an input can always be randomized to ensure that the state can be reconstructed with probability one. These results lead to an active state reconstruction algorithm.

Identification Input Design for Consistent Parameter Estimation of Linear Systems With Binary-Valued Output Observations

Input design is of essential importance in system identification for providing sufficient probing capabilities to guarantee convergence of parameter estimates to their true values. This paper presents conditions on input signals that characterize their probing richness for strongly consistent parameter estimation of linear systems with binary-valued output observations. Necessary and sufficient conditions on periodic signals are derived for sufficient richness. These conditions are further studied under different system configurations including open-loop and feedback systems, and different scenarios of noises including actuator noise, input measurement noise, and output measurement noise. In addition to system parameter estimation, essential properties of identifiability and input conditions are also derived when sensor thresholds or noise distribution functions are unknown. The findings of this paper provide a foundation to study identification of systems that either use binary-valued or quantized sensors or involve communication channels, which mandate quantization of signals.

Distributed Detection in Wireless Sensor Networks Using Dynamic Sensor Thresholds

This paper presents a new approach for distributed target detection in wireless sensor networks (WSNs). Contrary to the conventional practice where every sensor uses an identical threshold for decision-making, an unequal and dynamic local sensor threshold selection scheme is proposed. This threshold selection scheme is based on a recently proposed statistical metric for multiple testing problems called the False Discovery Rate (FDR). Assuming a signal attenuation model, where the received signal power decays as the distance from the target increases, various performance indices like the system level probability of detection and the probability of false alarm are studied. Simulation results are provided to demonstrate the effectiveness of this approach.

Optimal Control of Sensor Threshold for Autonomous Wide- Area-Search Munitions

The optimal employment of autonomous wide area search munitions is addressed. The scenario considered involves an airborne munition searching a battle space for stationary targets in the presence of false targets. Targets are modelled with uniform, Poisson, and normal distributions. False targets are modelled with Poisson distributions. All relevant parameters can be extracted from intelligence information on the enemy’s order of battle and the sensor performance specification. Analytic weapon effectiveness measures are derived using applied probability theory. The effectiveness measures derived in this paper handle time-varying parameters which characterize the battle space environment and the performance of the munition’s sensor. This allows the formulation and solution of optimization problems that maximize the probability of a target attack while at the same time constraining the probability of a false target attack. Optimal schedules for controlling the sensor threshold during the flight are derived and compared to the optimal constant-threshold results. An increase in weapon effectiveness is demonstrated when the sensor threshold is dynamically controlled during the flight.