A study of particle filtering approaches for the kidnapped robot problemTaylor, Clark N.; Mohler, David
doi: 10.1117/12.2305181pmid: N/A
Particle filtering is a popular approach to solving estimation problems that include non-linear, multi-modal, or other irregular structures in the estimation problem. Practically, however, some combinations of problems and implementations of the particle filter require a computationally unreasonable number of particles to achieve accurate estimation results. This is especially true as the number of dimensions in the state space increases. In this paper, we investigate one particular situation where a large number of particles may be required, the kidnapped robot problem. We implement several variants of the particle filter, evaluating which ones can best localize the robot after a “kidnapping” event without requiring too many particles to be practical. We find that significant improvements in performance are available using “particle flow” particle filter implementations.
Multilevel probabilistic target identification methodology utilizing multiple heterogeneous sensors providing various levels of target characteristicsHurley, Jeffery D.; Johnson, Clint; Dunham, Joel; Simmons, Jimmy
doi: 10.1117/12.2305186pmid: N/A
In modern systems, there are often many sensors which contribute to the identification of targets at various levels of identity amplification. Some sensors provide type or mode level identification while others provide unique fingerprints of the target of interest. This paper investigates combining of IDs from heterogeneous sensors in a probabilistic fashion to produce a fused multi-level identification. The identification of targets is especially difficult when sensors do not provide confidence metrics. When multiple sensors report differing identifications for the same target, the fusing of the results into a stable set of IDs is complicated. Often sensor integration systems are forced to toggle between candidate IDs that may not capture the breadth of the underlying sensor provided data. This paper describes a methodology for calculating a probabilistic ID based on the evaluation of provided identification data which provides intuitive results when faced with conflicting data. Conditions for choosing which calculation method to use are discussed based on the characteristics of each method.
An adaptive sensing approach for the detection of small UAV: first investigation of static sensor network and moving sensor platformLaurenzis, M.; Hengy, S.; Hammer, M.; Hommes, A.; Johannes, W.; Giovanneschi, F.; Rassy, O.; Bacher, E.; Schertzer, S.; Poyet, J.-M.
doi: 10.1117/12.2304758pmid: N/A
Fusion of information in heterogeneous multi-modal sensor networks has been proven to enhance sensing capabilities of ground troops to detect and track small unmanned aerial vehicles flying at low altitude. Nevertheless, the area coverage of a static sensor network could be permanently or temporally impacted by geographic topologies or moving obstacles which could reduce the local sensing probabilities. An additional moving sensor platform can be used to temporarily enhance sensing capabilities. First theoretical analysis and experimental field trials are presented using a static sensor network consisting of acoustical antenna array, a stationary FMCW RADAR and a passive/active optical sensor unit. Additionally, a measurement vehicle was applied, equipped with passive/active optical sensing devices. While the sensor network was used to monitor a stationary area with a sensor dependent sensing coverage, the measurement vehicle was used to obtain additional information outside the sensing range of the network or behind obstacles. A fusion of these data sets can provide an increased situational awareness. Limitations and improvements of this approach are discussed.
Robust spectral classificationTucker, Andrew W.; Kay, Steven
doi: 10.1117/12.2304616pmid: N/A
Spectral classification is a commonly used technique for discriminating between two or more signals. One popular approach to spectral classification utilizes the autoregressive model. In this model a white Gaussian random process is filtered by an all-pole filter. The autoregressive model leads to a classifier derived from the asymptotic Gaussian likelihood function. Despite substantial prior research effort put into developing a robust classifier, the ability of classifiers to discriminate between signals is not great and in some instances is not even satisfactory. A non-homogeneous Poisson process is an alternative way to model the power spectral density. This type of model leads to a different likelihood function, the realizable Poisson likelihood function. Monte Carlo simulations and data analyses demonstrate that the realizable Poisson likelihood function classifier is more robust then the asymptotic Gaussian classifier. The realizable Poisson likelihood function classifier has a greater probability of correct classification than the asymptotic Gaussian for signals with low signal-to-noise ratios, channel distortion, or certain pole locations.
A clutter-agnostic generalized labeled multi-Bernoulli filterMahler, Ronald
doi: 10.1117/12.2305464pmid: N/A
The labeled random finite set (LRFS) theory of B.-T. Vo and B.-N. Vo is the first systematic, theoretically rigorous formulation of true multitarget tracking, and is the basis for the generalized labeled multi-Bernoulli (GLMB) filter (the first implementable and provably Bayes-optimal multitarget tracking algorithm). Like most multitarget trackers, the GLMB filter is based on the assumption that clutter statistics are known a priori. Recent research has introduced RFS filters that are "clutter-agnostic," in the sense that they can address unknown, dynamically evolving clutter. These filters were unlabeled, however. In this paper we devise a clutter-agnostic GLMB (CA-GLMB) filter, based on the Bernoulli clutter-generator concept.
An analytic solution to ellipsoid intersections for multistatic radarShapero, Samuel A.
doi: 10.1117/12.2304836pmid: N/A
Unlike monostatic radars that directly measure the range to a target, multistatic radars measure the total path length from a transmitter, to the target, and then to the receiver. In the absence of angle information, the region of uncertainty described by such a measurement is the surface of an ellipsoid. In order to precisely locate the target, at least three such measurements are needed. In this paper, we derive from geometrical methods a general algorithmic solution to the intersection of three ellipsoids with a common focus. Applying the solution to noisy measurements via the cubature rule provides a solution that approaches the Cramer Rao Lower Bound, which we demonstrate via Monte-Carlo analysis. For conditions of low noise with non-degenerate geometries we also provide a consistent covariance estimate.
A framework for adaptive MaxEnt modeling within distributed sensors and decision fusion for robust target detection/recognitionKadar, Ivan
doi: 10.1117/12.2306146pmid: N/A
The Maximum Entropy (MaxEnt) information theoretic model parametric framework was introduced in a prior paper for distributed decision fusion (DDF) without knowledge of prior probabilities of local decisions. The paper demonstrated the effectiveness of the MaxEnt fusion center by achieving the best, realistic detection performance with respect to published results of either the Bayesian formulation or the Neyman-Pearson criterion. This paper represents the framework of an extension of MaxEnt DDF, called E-MaxEnt using: individual sensor MaxEnt classifiers for targets classification/recognition, and by fusing local classifier decisions. Specifically, in E-MaxEnt each sensor has a front-end pre-processing system for both signal detection and to process unique target attributes extracted for example from observed target imagery, which attributes are stored for reference/learning/comparison in the sensors MaxEnt classifiers. Based on the degree of match, each sensor generates local binary decisions that are sent to a MaxEnt fusion center, in the usual parallel architecture. No assumptions are made about knowing any local decision rules. The sensors are taking simultaneous (synchronized) measurements with overlapping FOV overages. It should be noted that the above description is not meant to address the “needle-in-haystack” problem, but rather address finding the presence, viz., classify/recognize a previously seen “known” target in areas where previously seen targets most likely are, along with other targets. At the time of writing, the data sets to test the algorithm were not available, but front-end image processing and MaxEnt classifiers were implemented. It is hoped that someone could provide the necessary data sets so the efficacy of the method could be demonstrated and compared with alternative approaches.
A generalized labeled multi-Bernoulli filter for correlated multitarget systemsMahler, Ronald
doi: 10.1117/12.2305463pmid: N/A
The labeled random finite set (LRFS) theory of B.-T. Vo and B.-N. Vo is the first systematic, theoretically rigorous formulation of true multitarget tracking, and is the basis for the generalized labeled multi-Bernoulli (GLMB) filter (the first implementable and provably Bayes-optimal multitarget tracking algorithm). Several of the author’s earlier papers investigated Bayes filters that propagate the correlations between two unlabeled evolving multitarget systems—but with limited success. In this paper we provide a theoretically rigorous and much more general approach, by devising a GLMB filter that propagates the correlations between two evolving labeled multitarget systems.
Low-cost multi-camera module and processor array for the ultra-fast framerate recognition, location, and characterization of explosive eventsYoedt, Cedric; Maraviglia, Carlos; Park, Sungjoo; Cox, Kevin
doi: 10.1117/12.2304459pmid: N/A
In the image processing world the detection, location, and identification of explosive events is accomplished usually by single detectors, single element detector arrays, or higher cost cameras (primarily infrared). Imaging systems have been limited by the too few event frames, high costs of components, and poor false alarm rates. For the last three years NRL’s Advanced Techniques Digital Technologies section has been researching ultra-fast framerate explosive event detection. NRL has designed, fabricated, and tested a multi-sensor array of low cost camera modules, each with its own field programmable gate array processor, which are then networked together to implement a system capable of imaging explosive events at 16-30kHz framerates in real time. These camera modules work in the visible band and open up the possibility of exploiting 30-60 frames of an explosive event. With this array it is possible not only to image burning gases and high intensity flashes but also low signature moving effluent and airborne particles. By using processors behind each camera module it is possible to leverage different parts of the algorithm to accomplish computationally expensive operations on individual frames. Networking the array together allows further distribution of the processing for further temporal analysis. Finally all of the resulting images are sent to a central processor where the final parts of the algorithm are completed. The cost of this system once optimized for production will be close to that of acoustic systems but with much higher precision.
Error statistics of bias-naïve filtering in the presence of biasChance, Zachary; Relyea, Stephen; Anderson, Evan
doi: 10.1117/12.2303765pmid: N/A
In the field of sensing, a typically unavoidable nuisance is the inherent bias of a sensor due to imperfections in timing, calibration, and other sources. The errors incurred by the bias ripple through higher-level processes such as tracking and sensor fusion, causing varying effects to each operation. In many different applications, such as track-to-track correlation, the overall effect of the biases on state estimation is modeled as a constant, translational shift in the position dimension of the track states. This assumption can be appropriate when the required precision of the track states is not stringent. However, in general, sensor bias can not only affect position estimates but also positional derivatives, i.e., velocity, acceleration, in a manner that can change dramatically depending on sensor-target geometry; for situations where high state estimation accuracy is required, these consequences become apparent and need to be handled. The contribution from measurement bias to state estimation error depends on many different aspects, e.g., measurement uncertainty, dynamic model uncertainty, sensor-target geometry. The focus of this work is the quantification of the relative significance of measurement error and measurement bias in the resultant state estimation error. In short, using the results in this work, it is straightforward to: (i) determine regimes where measurement bias becomes a predominant factor, (ii) bound the impact of the sensor bias on the outputted tracking information, (iii) analyze the dependence of the tracking error on sensor-target geometry, all of which can be of great impact when designing a tracking system architecture.