KEYWORDS: Sensors, Data communications, Monte Carlo methods, Data fusion, Time metrology, Error analysis, Detection and tracking algorithms, Sensor fusion, Stochastic processes, Lithium
Recently, the author solved a general problem of fusing asynchronous tracks while taking
into account communicatrion delays, data latency, and out of sequence tracks. The objective of
this paper is to perform preliminary performance analysis of the asynchronous track fusion
algorithm. In this study, two snesors providing asynchronous measurements are considered,
where the update track fusion rate is fixed. Communication delay between at least of the senors'
platform and the fusion center may exit. Monte Carlo simulations are performed using simulated
traget tracks. The performance of the individual sensors as well as that of the fused track is
provided. The preliminary results show the benefit of track fusion under more realistic
assumptions than what is currently the practice.
This paper documents the design and development of a low cost robot capable of autonomous navigation in unknown
indoor environments. The proposed design uses only two complementary rotating sensors for navigation. The use of real
time mapping allows for detection and avoidance of obstacles. The fusion of the sensors data helped improve accuracy
of the online map of the robot environment. The robot builds an online map of its environment, and then automatically
plans its navigation path. The feedback control keeps the robot moving along its planned path. The robot has been
successfully tested in a cluttered environment in the Advanced Systems Lab. Preliminary tests carried out have shown
the success of the robot in navigating autonomously.
Target tracking with radar and sonar is done in either spherical or rectangular coordinates. Often tracking is done in one reference frame while filtering, usually Kalman, is done in another reference frame. It is commonly assumed that the probability density functions can be treated the same in both reference frames. An extended Kalman filter is used under this assumption that the probability density function of the measurements after conversion can be adequately characterized by the mean and standard deviation. The transformations from spherical coordinates (see manuscript) to Cartesian coordinates (see manuscript) is a non-linear transformation, so the statistical characteristics of the measurement process noise are changed significantly by the transformation. Thus, the characteristics which tracking filters are designed to optimize with respect to are changed as well by these coordinate transformations. Typical engineering practice uses approximations rather than exact solutions. The objective of this paper is to provide means to analytically characterize the probability density functions of these coordinate transformations. We then investigate the impact of approximating the noise statistics of these transformed coordinates on track quality.
This paper provides a simplified solution to the general asynchronous track fusion problem of the authors. The original solution solves a practical sensor to sensor track fusion problem when the sensors used are asynchronous, communication delays exist between sensor platforms and track fusion center, and tracks may arrive out-of-sequence. The new fusion rule is derived under mild assumptions. The rule does not require the synchronization of tracks for the purpose of track fusion. The Bar-Shalom-Campo fusion rule is derived as a special case of the new rule for the case of synchronous tracks. This rule is also illustrated on an alpha-beta filter.
In large volume pharmaceutical mail order, before shipping out prescriptions, licensed pharmacists ensure that the drug in the bottle matches the information provided in the patient prescription. Typically, the pharmacist has about 2 sec to complete the prescription verification process of one prescription. Performing about 1800 prescription verification per hour is tedious and can generate human errors as a result of visual and brain fatigue. Available automatic drug verification systems are limited to a single pill at a time. This is not suitable for large volume pharmaceutical mail order, where a prescription can have as many as 60 pills and where thousands of prescriptions are filled every day.
In an attempt to reduce human fatigue, cost, and limit human error, the automatic prescription verification system (APVS) was invented to meet the need of large scale pharmaceutical mail order. This paper deals with the design and implementation of the first prototype online automatic prescription verification machine to perform the same task currently done by a pharmacist. The emphasis here is on the visual aspects of the machine. The system has been successfully tested on 43,000 prescriptions.
This paper solves a general track fusion problem with feedback when the sensors used are asynchronous, communication delays exist between sensor platforms and track fusion center, and tracks may arrive out- of-sequence. For the proposed linear fusion rule, the solution is shown to be optimal in the minimum mean square sense. The batch processing of incoming tracks provides an elegant solution for the out-of-sequence and latent tracks without special processing of such data.
Tracking maneuvering targets has always been a significant challenge to the tracking community, so new approaches to this problem are always being pursued. One approach is to use multiple model filters as an attractive design logic for both maneuver detection and filter re-initialization. Common current practice in multiple model tracking uses a switching Markov model. A well known multiple model tracker that uses Markov switching model is the Interactive Multiple Model (IMM). This approach requires the a priori knowledge of the transition probability matrix (TPM) of the target state. Such knowledge may not be available unless one has combat identification, so one is usually dealing with target of unknown maneuver strategies. The objective of this paper is to introduce concepts from fuzzy sets to design a multiple model filter which is applicable to an arbitrary number of target models while at the same time not requiring the usage of the Markov switching to transition between the threat models. The essential concept is to treat each possible target dynamics as a fuzzy cluster, then to use the measurement information about the target to compute the degree of membership the target has relative to a particular fuzzy cluster. Such membership value would then be the equivalent of the switching gain when using IMM terminology. The target state is the weighted sum of the states provided by each individual filter.
Tracking maneuvering target using multiple models is an attractive approach that is an alternative to a design that needs logic for both maneuver detection and filter re-initialization. Common current practice in multiple model tracking uses a switching Markov model. Recently the authors developed a multiple model approach to tracking maneuvering targets, but without using a switching Markov model. The models used work independently, while the process noise of each is adjusted online based on a relative likelihood function. The performance and consistency of the newly developed filter are compared to an IMM tracker.
KEYWORDS: Sensors, Filtering (signal processing), Monte Carlo methods, Electronic filtering, Data processing, Sensor fusion, Time metrology, Error analysis, Data fusion, 3D acquisition
Recently the authors developed a new filter that uses data generated by asynchronous sensors to produce a state estimate that is optimal in the minimum mean square sense. The solution accounts for communications delay between sensors platform and fusion center. It also deals with out of sequence data as well as latent data by processing the information in a batch-like manner. This paper compares, using simulated targets and Monte Carlo simulations, the performance of the filter to the optimal sequential processing approach. It was found that the new asynchronous Multisensor track fusion filter (AMSTFF) performance is identical to that of the extended sequential Kalman filter (SEKF), while the new filter updates its track at a much lower rate than the SEKF.
Tracking maneuvering target using multiple models is an attractive approach that is an alternative to a design that needs logic for both maneuver detection and filter re-initialization. Common current practice in multiple model tracking uses a switching Markov model. This paper also uses a multiple model approach to tracking maneuvering targets, but without using a switching Markov model. The models used work independently, while the process noise of each is adjusted online based on a relative likelihood function. The performance of the newly developed filter will be compared to an IMM tracker.
Sensor data fusion has long been recognized as a means to improve target tracking. Common practice assumes that the sensors used are synchronous, i.e., they have identical data rate, measurements are taken at the same time, and have no communication delays between sensors platform and central processing center. Such assumptions are invalid in practice. Previous work of the authors dealt designing asynchronous track fusion filter that removes such assumptions when considering the multi-sensor target tracking case. This paper deals with the existence of a solution to the asynchronous track fusion problem for the case of three asynchronous sensors. In addition, the performance deterioration of the filter is analyzed as a function of the track fusion update rate for CV targets.
KEYWORDS: Lithium, Detection and tracking algorithms, Filtering (signal processing), Data analysis, Lutetium, Data processing, Network centric warfare, Data fusion, Signal processing, Information operations
What underpins this vision as axiomatic is the mantra information is power. Besides the necessary requirement of information exchange networks with sufficient bandwidth and computational power to treat the data being passed around the network; algorithms are required to make sense of the data. It is estimation algorithms that turn the straw (data) into gold (information). Both proper execution and improvements in estimation algorithms are the enabling technology that facilitates the formation and usage of data across the envisioned warfare networks. We focus on some of the requirements that are driving the formation of these networks from a surface navy perspective in terms of estimation. We also discuss how these requirements focus the design of potentially new algorithms. We also discuss some of the crucial issues that may drive future requirements and algorithms.
Sensor data fusion has long been recognized as a means to improve target tracking. Common practice assumes that the sensors used are synchronous, i.e., they have identical data rate, measurements are taken at the same time, and have no communication delays between sensors platform and central processing center. Such assumptions are invalid in practice. Previous work of the authors dealt designing asynchronous track fusion filter that removes such assumptions when considering the multi-sensor target tracking case. This paper deals with the existence of a solution to the asynchronous track fusion problem for the case of two asynchronous sensors. In addition, the performance deterioration of the filter is analyzed as a function of the track fusion update rate.
Sensor data fusion has long been recognized as a means to improve target tracking. Common practice assumes that the sensors used are synchronous (i.e., perform the same operation at the identical time), take measurements at the same time and have no communication delays between sensor platforms and the central processing center. Such assumptions are not valid in practice. This paper removes these assumptions when dealing with multisensor target tracking. In particular, it assumes that the sensors used can have different data rates and communication delays, between local and central platforms. A new tracking algorithm using asynchronous sensors is proposed and derived in this paper.
KEYWORDS: Sensors, Data communications, Monte Carlo methods, Data processing, Data fusion, Matrices, Error analysis, Computing systems, Target recognition, Sensor fusion
Sensor data fusion has long been recognized as a means to improve target tracking. Common practice assumes that the sensors used an synchronous, i.e., they have identical data rate, measurements are taken at the same time, and have no communication delays between sensors platform and central processing center. Such assumptions are invalid in practice. This paper deals with removing such assumptions when considering the multi-sensor target tracking case. In particular, it assumes that the sensors used can have different data rates and communication delays between local and central platforms. A new target tracker using asynchronous sensors is proposed and derived in this paper. The performance of the filter is compared to the optical sequential filter using simulated targets.
The Interactive Multiple Model (IMM) algorithm is a well known multiple model technique for tracking maneuvering targets. In its design, the IMM assumes that the target transition likelihood is known a priori. However, such an assumption is violated in practice. The objective of this paper is to design a multiple model tracker without assuming any a priori knowledge about the characteristics of the target motion. The performance of the newly developed filter will be compared to an IMM tracker.
KEYWORDS: Error analysis, Detection and tracking algorithms, Filtering (signal processing), Sensors, Switching, Process modeling, Performance modeling, Time metrology, Monte Carlo methods, Kinematics
A numeric solution for the fusion of multiple tracks produced form an arbitrary number of asynchronous measurements has been recently developed. This track fusion solution is a weighted sum of the values associated with the previous fused estimate and the multiple individual estimates. This optimal asynchronous track fusion algorithm (OATFA) has properties that are identical to the Kalman filter. However, the deficiencies of the Kalman filter when tracking maneuvering targets are also exhibited by the OATFA but can be overcome with the use of the Interacting Multiple Model (IMM) algorithm. Consequently, it should be possible to replace the Kalman filter commonly employed in a conventional IM algorithm with the OATFA to from the IMM- OATFA. The IMM-OATFA will be developed and simulation result will be used to compare this performance with a conventional IMM tracker.
KEYWORDS: Sensors, Error analysis, Filtering (signal processing), Data processing, Radar, Algorithm development, Monte Carlo methods, Time metrology, Detection and tracking algorithms, Data fusion
An analytic solution for the fusion of track estimates produced from two asynchronous measurements has been recently developed. The fusion process can occur at any time in the interval between the arrival of the second measurement of a fusion interval and the first measurement of the next fusion interval. The solution was stipulated to be a weighted sum of the previous fused estimate and the two individual estimates. The matrix weights are the unknowns for which a solution was formulated. This fusion process has properties that are identical to the Kalman filter. Even though this technique is a breakthrough, it is restricted to the fusion of only two estimates. The objective of this paper is to provide a numeric solution to this track fusion problem with an arbitrary number of asynchronous measurements. Simulation results will be employed to compare the performance of the Kalman filter and the track fusion algorithm in a multisensor environment.
Modern command and control systems depend on surveillance subsystems to form an overall tactical pictures. The use of sensors with different capabilities can improve the quality of the aggregate picture. However, the quality of the fused data is highly dependent on the quality of the data that is supplied to the fusion processor. Before the fusion process takes place, sensor data has to be transformed to a common reference frame. Since each individual sensor's data may be biased, a prerequisite for successful data fusion is the removal of the bias errors contained in the data from all contributing sensors. In this paper, a technique is developed to perform absolute sensor alignment (the removal of bias errors) using information from moving objects, such as low earth orbit satellites, that obey Kepler's laws of motion.
KEYWORDS: Control systems, Computer programming, Telecommunications, Control systems design, Signal detection, Servomechanisms, Process control, Analog electronics, Robotics, Safety
Recently the ASL at Tennessee Technological University was donated a GEP50 welder. The welding is done via off line point-to-point teaching. A state of the art robot was needed for research but because money was not available to purchase such an expensive item. It was therefore decided to upgrade the GEP50 control system to make the welder a multitasking robot. The robot has five degrees of freedom can be sufficient to pursue some research in robotics control. The problem was that the control system of the welder is limited to point-to-point control, using off-line teaching. To make the GEP50 a multitasking robot that can be controlled using different control strategies, the existing control system of the welder had to be replaced. The upgrade turned to be a low cost operation. This robot is currently in sue to test different advanced control strategies in the ASL. This work discusses all the steps and tasks undertaken during the upgrade operation. The hardware and software required or the upgrade are provided in this paper. The newly developed control system has been implemented and tested successfully.
KEYWORDS: Sensors, Data communications, Filtering (signal processing), Data fusion, Data processing, Error analysis, Monte Carlo methods, Data centers, Digital filtering, Detection and tracking algorithms
The theory behind the fusion of two asynchronous track updates has been derived by the authors in a previous paper. However, the result were incomplete since there was not sufficient information to actually implement the fusion theory as an algorithm. In this paper, the information necessary to implement the theory is provided. Additionally, simulated data in the form of tracks from two sensors having different update rates and communications delays is used to compare this technique to the result obtained using the standard sequential updating of a Kalman filter.
This paper deals with tube leak detection in industrial boilers. A decentralized information processing approach is used to detect and isolate the location of boilers tube leaks. Tube leak sensitive variables (TLSV) are used as the information source for detection and isolation. Such variables are already collected by the system for the purpose of control and monitoring. Given the TLSV, artificial neural networks are used to detect the presence of a leak and its location in the boiler. The proposed approach was successfully applied to tube leak detection and isolation in five subsystems of a utility boiler.
KEYWORDS: Switching, Monte Carlo methods, Kinematics, Process modeling, Sensors, Electronic filtering, Target detection, Motion models, Coastal modeling, Systems modeling
This paper presents a new tracking filter capable of soft switching between two kinematic target models without requiring any a prior knowledge of the target state's transition probability matrix. The target models used are both constant velocity models, one with a low state process noise and one with a high state process noise. Simulations are performed to show the soft switching capability of the new filter as well as its performance. The newly derived filter significantly outperforms a well-known variable dimension filter. The result of this paper constitute a first step toward designing a new class of filters that are capable of soft switching between different target kinematic models without requiring a priori knowledge of the target state's transition likelihoods.
KEYWORDS: Switching, Motion models, Coastal modeling, Systems modeling, 3D modeling, Kinematics, Sensors, Matrices, Optimal filtering, Monte Carlo methods
This paper investigates the existence and design of optimal, multiple model filters for target tracking using different local models. Two models are used: a constant velocity and a constant acceleration model. Sufficient conditions are derived that guarantee the existence of optimal multiple model filters. The problem is then solved for the case where the switching gains are diagonal matrices. The performance of the derived multiple model filter is evaluated using a simulated target trajectory.
Consider two sensors tracking a single target and the two tracks of this target, one from each sensor, that are generated. For some time, it has been recognized that these two tracks are correlated and that this correlation is due to the common process noise of the target. A MMSE solution has been derived that accounts for this correlation between tracks when fusing tracks from synchronous sensors. Here, synchronous means that the sensor take measurements of the target's position at the same time and that they arrive at the fusion center with no communications delays. This paper extends the previous work in correlated track fusion to include asynchronous sensors, i.e., the sensor do not measure the target's position at the same time, and the possibility of communications delays.
KEYWORDS: Artificial neural networks, Model-based design, Systems modeling, Data processing, Sensors, Complex systems, Neural networks, Signal detection, Signal processing, Process modeling
This paper presents the design of a systematic failure detection and isolation system that uses the concept of failure sensitive variables (FSV) and artificial neural networks (ANN). The proposed approach was applied to tube leak detection in a utility boiler system. Results of the experimental testing are presented in the paper.
KEYWORDS: Sensors, Detection and tracking algorithms, Optical tracking, Data communications, Monte Carlo methods, Data fusion, Optical engineering, Data processing, Computer simulations, Algorithm development
KEYWORDS: Sensors, Algorithm development, Detection and tracking algorithms, Data communications, Monte Carlo methods, Computer simulations, Data processing, Information technology, Lithium, Sensor fusion
In practice, multisensor systems use dissimilar sensors having different data rates. Such sensors may also have inherent delays as well as communication delays. Recently the authors developed a track fusion algorithm that attempted to account for realistic constraints of sensor fusion. The objective of this paper are two fold. First, it shows that the synchronous track fusion problem can be derived as a special case of the developed track fusion algorithm. Second, using simulated target tracks, the performance of the asynchronous track fusion algorithm is analyzed and compared to an existing fusion algorithm. Different sensor data rates and communication delays are used in the simulations.
KEYWORDS: Sensors, Detection and tracking algorithms, Data communications, Data processing, Electrical engineering, Warfare, Data fusion, Filtering (signal processing), Polonium
This paper proposes a track fusion algorithm for similar and dissimilar synchronous sensors. It is shown that under communication requirements between the fusion center and the local remote station, the proposed algorithm is optimal in the minimum mean square sense.
In tracking maneuvering targets, ascertaining the start and the end of a maneuver time is a prerequisite to maintaining good track quality. Existing maneuver detection schemes based on point-mass tracking are either inadequate to reliably detect rapid maneuvers or computationally impractical. Maneuver detection based on images can be more accurate but is also very demanding on computational and storage resources. A new method of detecting maneuvers using information from an image sensor is presented. It involves the use of two detectors, one to detect linear acceleration and the other to detect orientation changes of the target. To detect target maneuvers, the detectors continuously monitor the position of three points extracted from the target image-the centroid, the front centroid, and the rear centroid. Contrary to existing image-based tracking methods, the approach put forth is very attractive from computational and storage points of view. The proposed method was tested using simulated tracks and the results confirm the benefits of its utilization for maneuver detection.
In tracking maneuvering targets, the detection of the start and the end of a maneuver time is a prerequisite to maintaining good track quality. The paper addresses the issue of maneuver detection using information from an image sensor. Contrary to existing image sensor based tracking methods, the proposed approach is very attractive from computational and storage points of view.
The objective of this paper is to examine the performance of the sequential approach and an asynchronous data fusion approach to target tracking as defined for two dissimilar sensors. First, analytical expressions for evaluating the performance of these two approaches were derived. Then, these expressions were applied to the fusion of data from a multi-tasking radar and an optical sensor. It is assumed that the data rate of the optical sensor is much higher than the radar's. Overall, it was found that the sequential and data fusion approaches have reasonably similar performance for this case. However, the computational advantage of the fusion approach makes it more attractive than the sequential approach.
This paper presents a technique for the estimation and removal of sensor biases and sensor frame misalignment errors for netted 3-D radar systems. One radar is assumed to have no bias in its measurements, and no tilt errors in its reference frame. The algorithm involves a two stage process. The first stage involves estimating the bias of each sensor and removing the effects of that bias with the estimate. The second stage uses the `bias-free' sensor measurements to estimate the sensor frame tilt errors. Simulation results are presented to demonstrate the performance of this approach.
KEYWORDS: Radar, Data fusion, Optical sensors, Sensors, Optical testing, Optical tracking, Monte Carlo methods, Data processing, Radar sensor technology, Algorithm development
A technique is developed for fusing asynchronous data from two dissimilar sensors, where one sensor provides data at a high rate relative to the other. The idea is to obtain a least-squares estimate of the high data rate sensor data at the time when the other sensor observation is taken. A previously developed synchronous data fusion algorithm is then used to fuse the time aligned data for updating the target state estimates. The case of fusing data from an optical sensor that provides periodic data at a high rate and a radar that provides quasi-periodic data at a low data rate is considered. The performance of a track filter utilizing this data fusion approach is shown via simulation to provide results that are similar to those obtained by the standard sequential data processing approach that requires significantly more computations.
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