Mark P. Kolba

Information-Based Sensor Management in the Presence of Uncertainty

Recognizing the uncertainty present in all real-world problems, this correspondence extends the mathematical framework of a previously presented information-based sensor manager to permit operation with uncertain sensor probabilities of detection and false alarm. Simulation results demonstrate the importance of proper uncertainty modeling for the attainment of robust sensor manager performance

A Framework for Information-Based Sensor Management for the Detection of Static Targets

A framework is presented for information-theoretic sensor management for the detection of static targets. The sensor manager searches for targets within a cell grid using a suite of sensor platforms. Each sensor platform may contain one or more sensing modalities, and each of these modalities has known probabilities of detection and false alarm and also has an associated cost of use. Additional information such as motion constraints on the sensors and the prior distribution of the targets in space is incorporated. The sensor manager then directs the movement of the sensors through the grid by maximizing the expected information gain that will be obtained with each new sensor observation. Key modeling questions are addressed, including the selection of an appropriate information measure and the joint or independent management of the sensors. Through a number of simulations, the performance of the sensor manager is compared to the performance of a blind sweep procedure, a random search procedure, and an alternative information-theoretic sensor manager. The intelligent sensor management procedure is demonstrated to achieve a superior performance compared to all of the other three techniques. A specific application area for which the sensor management problem is becoming more critical is landmine detection; thus, the performance of the sensor manager is also analyzed using real data from three different landmine detection sensing modalities, and the proposed sensor management technique is again demonstrated to be superior compared to more simplistic approaches.

Information-based sensor management for landmine detection using multimodal sensors

We consider an information-theoretic approach for sensor management that chooses sensors and sensor parameters in order to maximize the expected discrimination gain associated with each new sensor measurement. We analyze the problem of searching for N targets with M multimodal sensors, where each sensor has its own probability of detection, probability of false alarm, and cost of use. Other information, such as the prior distribution of the targets in space and the degree of constraint of the sensor motion, is also utilized in our formulation. Performance of the sensor management algorithm is then compared to the performance of a direct-search procedure in which the sensors blindly search through all cells in a predetermined path. The information-based sensor manager is found to have significant performance gains over the direct-search approach. Algorithm performance is also analyzed using real landmine data taken with three different sensing modalities. Detection performance using the sensor management algorithm is again found to be superior to detection performance using a blind search procedure. The simulation and real-data results also both illuminate the increased performance available through multimodal sensing.

Information-theoretic Sensor Management for Multimodal Sensing

In recent years, sensor management algorithms have been studied for the purpose of providing intelligent, automated control of complex fielded sensor suites in remote sensing applications. In this paper, a framework for sensor management is presented that is based on the information-theoretic formulation of Kastella. The sensor manager searches for N targets using M multimodal sensing platforms and incorporates realistic features such as cost of motion and cost of use for the sensors as well as the availability of useful prior information about the region of interest. In all cases, the performance of the sensor manager is found to be superior to a direct search procedure in which the sensors methodically sweep through the cell grid. Sensitivity of the sensor manager to erroneous prior information is also examined, and the sensor manager performance is found to be robust to reasonable errors in the prior information. Finally, the sensor manager is demonstrated to perform successfully on a set of real landmine data.

Sensor management using a new framework for observation modeling

In previous work, a sensor management framework has been developed that manages a suite of sensors in a search for static targets within a grid of cells. This framework has been studied for binary, non-binary, and correlated sensor observations, and the sensor manager was found to outperform a direct search technique with each of these different types of observations. Uncertainty modeling for both binary and non-binary observations has also been studied. In this paper, a new observation model is introduced that is motivated by the physics of static target detection problems such as landmine detection and unexploded ordnance (UXO) discrimination. The new observation model naturally accommodates correlated sensor observations and models both the correlation that occurs between observations made by different sensors and the correlation that occurs between observations made by the same sensor. Uncertainty modeling is also implicitly incorporated into the observation model because the underlying parameters of the target and clutter cells are allowed to vary and are not assumed to be constant across target cells and across clutter cells. Sensor management is then performed by maximizing the expected information gain that is made with each new sensor observation. The performance of the sensor manager is examined through performance evaluation with real data from the UXO discrimination application. It is demonstrated that the sensor manager is able to provide comparable detection performance to a direct search strategy using fewer sensor observations than direct search. It is also demonstrated that the sensor manager is able to ignore features that are uninformative to the discrimination problem.

The effects of uncertainty and uncertainty modeling on information- based sensor manager performance

A proliferation of the number and variety of sensors for the landmine detection problem has created the need for a sensor manager that is able to intelligently task and coordinate the operation of a suite of landmine sensors. Previous work has developed a framework for sensor management that takes into account the context of the landmine detection problem. The sensor manager searches for N targets in a grid using M multimodal sensors by seeking to maximize the expected information gain. The probabilities of detection and false alarm of the sensors are assumed to be known and are used in the sensor manager calculations. However, in a real-world landmine detection setting, the performance characteristics of the sensors will in fact be unknown. Uneven and irregular ground, vegetation, unanticipated clutter objects, even bad weather - all these can affect the performance of a landmine sensor. This paper examines the effects of uncertainty in the probabilities of detection and false alarm on the performance of the previously presented sensor manager and further examines the performance effects of properly and improperly modeling this uncertainty. Performance is, naturally, found to be adversely affected by uncertainty. However, it is demonstrated that properly modeling the uncertainty present in the problem helps to recover some of the performance that is lost through the introduction of uncertainty.

Analysis of an Information-Based Sensor Manager Applicable to Landmine Detection

Previously, a framework for sensor management has been developed for the detection of static targets such as landmines. The sensor manager functions by tasking the available sensors to greedily maximize the expected information gain obtained with each new sensor observation. This paper examines several of the key assumptions and decisions that were made in the formulation of this sensor manager to assess both the validity and the performance effects of these decisions. Specifically, this paper examines which unmanaged sensing technique is best used to make performance comparisons with the sensor manager, whether multiple sensors should be optimized independently or jointly, and finally whether the Kullback-Leibler divergence or Renyi divergence is the best choice of information measure to use. This paper demonstrates that in all three cases, the choices made in the original formulation of the sensor manager are the most effective and appropriate.

Information‐based sensor management for landmine detection using electromagnetic induction, ground‐penetrating radar, and seismic sensors

An information‐based sensor management framework is presented that enables the automated tasking of a suite of sensors when detecting static targets. The sensor manager chooses the sensors to use and the grid‐based locations to observe in order to maximize the expected information gain that will be obtained with each new sensor observation. Initially, sensor probabilities of detection and false alarm, Pd and Pf, are assumed to be known by the sensor manager. In a field setting, however, Pd and Pf cannot be known exactly, and so uncertainty modeling for Pd and Pf is also presented. The sensor manager is tested on real landmine data using electromagnetic induction (EMI), ground‐penetrating radar (GPR), and seismic sensors. A matched subspace detector is used to process the EMI data, an adaptive pre‐screening algorithm based on the least mean squares (LMS) adaptive filter is used to process the GPR data, and whitening followed by an energy detector is used to process the seismic data. The sensor manager is a...

Fusion of ground-penetrating radar and electromagnetic induction sensors for landmine detection and discrimination

Ground penetrating radar (GPR) and electromagnetic induction (EMI) sensors provide complementary capabilities in detecting buried targets such as landmines, suggesting that the fusion of GPR and EMI modalities may provide improved detection performance over that obtained using only a single modality. This paper considers both pre-screening and the discrimination of landmines from non-landmine objects using real landmine data collected from a U.S. government test site as part of the Autonomous Mine Detection System (AMDS) landmine program. GPR and EMI pre-screeners are first reviewed and then a fusion pre-screener is presented that combines the GPR and EMI prescreeners using a distance-based likelihood ratio test (DLRT) classifier to produce a fused confidence for each pre-screener alarm. The fused pre-screener is demonstrated to provide substantially improved performance over the individual GPR and EMI pre-screeners. The discrimination of landmines from non-landmine objects using feature-based classifiers is also considered. The GPR feature utilized is a pre-processed, spatially filtered normalized energy metric. Features used for the EMI sensor include model-based features generated from the AETC model and a dipole model as well as features from a matched subspace detector. The EMI and GPR features are then fused using a random forest classifier. The fused classifier performance is superior to the performance of classifiers using GPR or EMI features alone, again indicating that performance improvements may be obtained through the fusion of GPR and EMI sensors. The performance improvements obtained both for pre-screening and for discrimination have been verified by blind test results scored by an independent U.S. government contractor.

Sensor management for landmine detection using correlated sensor observations

Previous research has developed an information-theoretic sensor management framework for improving static target detection performance. This framework has been successfully applied to a large dataset of real landmine data; performance using the sensor manager on this dataset was demonstrated to be superior to performance using a direct search technique in which sensors blindly sweep through the gridded region of interest. In previous work, the sensor manager has modeled the observations made in each grid cell as being independent from the other observations made in that cell by the same sensor and also as being independent from observations made in that cell by other sensors. Such a modeling approach fails to account for the correlations that will result between observations made both by the same and different sensors. This paper alters the modeling framework that has been used previously to incorporate observation correlation, which will more realistically model the interrelationships between sensor observations. After introducing the new modeling approach, results are then presented that compare the performance of the sensor manager to the performance of an unmanaged direct search procedure. The sensor manager is again demonstrated to outperform direct search. Furthermore, the performance effects of modeling and failing to model correlation are examined through simulation. Failing to model correlation that is present in the data is demonstrated to substantially degrade performance and cause direct search to outperform the sensor manager. However, when correlated modeling is used to model correlated data, the sensor manager is again demonstrated to outperform direct search.