Effective missile warning and countermeasures are an unfulfilled goal for the Air Force and DOD community. To make the expectations a reality, sensors exhibiting the required sensitivity, field of regard, and spatial ...
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Effective missile warning and countermeasures are an unfulfilled goal for the Air Force and DOD community. To make the expectations a reality, sensors exhibiting the required sensitivity, field of regard, and spatial resolution are needed. The largest concern is the first stage of a missile warning system, detection, in which all targets need to be detected with a high confidence and with very few false alarms. Typical sensors are limited in their detection capability by the presence of heavy background clutter, sun glints, and inherent sensor noise. Many threat environments include false alarm generators like burning fuels, flares, exploding ordinance, and industrial sources. Multicolor discrimination is one of the most effective ways of improving the performance of infrared missile warning sensors, particularly for heavy clutter situations. Its utility has been demonstrated in fielded scanning sensors. Utilization of the background and clutter spectral content, coupled with additional spatial and temporal filtering techniques have resulted in a robust real-time algorithm to increase signal-to-clutter ratios against point targets. Algorithm results against tactical data are summarized and compared in terms of computational cost as implemented on a real-time 1024 SIMD machine.
For the landmine detection problem, a detector that provides a high probability of detection and a low probability of false alarm is needed. It is often the case that detectors satisfy one requirement at the cost of p...
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ISBN:
(纸本)081943664X
For the landmine detection problem, a detector that provides a high probability of detection and a low probability of false alarm is needed. It is often the case that detectors satisfy one requirement at the cost of poor performance with regard to the other. Single sensors cannot achieve this goal, since every sensor has its advantages and disadvantages when dealing with a large variety of landmines, from large metal-cased mines to small plastic-cased mines, etc. Thus, in this paper we consider two types of sensors, EMI and GPR. Time-domain EMI has been extensively used in the military and humanitarian demining. However, it is essentially a metal detector, thus, can detect mines with high metal content successfully, as well as metal debris in the environment. This yields poor detection performance on mines with low metal content and high false alarm sate if the field was contaminated by metallic clutter. On the other hand, GPR is a potential tool for landmine detection, since it can detect and identify subsurface anomalies. A GPR system with wide frequency band can achieve good resolution and adequately deep penetration for landmine detection. In our previous work, we have shown that Bayesian detection approach can be applied to EMI data and provide promising results. In this paper, we present results that indicate that statistical signalprocessing techniques can improve performance over the conventional detection methods, which are usually based on the energy present in the signal. Specifically, we consider data taken by the Coleman Research Corporation (CRC) Handheld Standoff Mine Detection System (HSTAMIDS) at Fort A. P. Hill, VA. and Yuma, AZ. The active system of the HSTAMIDS contains co-located metal detector (MD) and GPR sensors, which allows us to fuse the data from the MD and GPR sensors. Thus, in addition to discussing individual sensor dataprocessing, we also present result of data fusion of both the MD and the GPR data using the HSTAMIDS system.
Sleep spindles are important short-lasting waveforms in the sleep EEC They are the hallmarks of the so-called Stage 2 sleep. Automated methods for spindle detection presented in literature typically use some form of f...
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We consider the use of meta-data and/or video-domain methods to detect similar videos on the web. Meta-data is extracted from the textual and hyperlink information associated with each video clip. In video domain, we ...
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We consider the use of meta-data and/or video-domain methods to detect similar videos on the web. Meta-data is extracted from the textual and hyperlink information associated with each video clip. In video domain, we apply an efficient similarity detection algorithm called video signature. The idea is to form a signature for each clip by selecting a small number of its frames that are most similar to a set of random seed images. We then apply a statistical pruning algorithm to allow fast detection on very large databases. Using a small ground-truth set, we achieve 90% recall and 95% precision using only 8% of the total number of operations required without pruning. For a database of around 46,000 video clips crawled from the web, video signature technique significantly outperforms meta-data in precision and recall. We show that even better performance can be achieved by combining them together. Based on our measurements, each video clip in our database has, on average, 1.53 similar copies.
Cerebellum is important in controlling, fine-tuning and predicting movements. Here a mathematical model of the cerebellar granule neuron was developed to help to explain the dynamics of the non-linear ionic currents u...
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Previous nonlinear filtering research has shown that by directly estimating the probability density of the target state, weak and closely spaced targets can be tracked without performing data association. data associa...
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ISBN:
(纸本)081943678X
Previous nonlinear filtering research has shown that by directly estimating the probability density of the target state, weak and closely spaced targets can be tracked without performing data association. data association imposes a heavy burden, both in its design where complex data management structures are required and in its execution which often requires many computer cycles. Therefore, avoiding data association can have advantages. However, some have suggested that data association is required to estimate and correct sensor biases that are nearly always present so avoiding it is not a practical option. This paper demonstrates that target numbers, target tracks, and sensor biases can all be estimated simultaneously using association-free nonlinear methods, thereby extending the useful range of these methods while preserving their inherent advantages.
In this paper, a small moving object method detection method in video sequences is described. In the first step, the camera motion is eliminated using motion compensation. An adaptive subband decomposition structure i...
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In this paper, a small moving object method detection method in video sequences is described. In the first step, the camera motion is eliminated using motion compensation. An adaptive subband decomposition structure is then used to analyze the motion compensated image. In the 'low-high' and 'high-low' subimages small moving objects appear as outliers and they are detected using a statistical Gaussianity detection test based on higher order statistics. It turns out that in general, the distribution of the residual error image pixels is almost Gaussian. On the other hand, the distribution of the pixels in the residual image deviates from Gaussianity in the existence of outliers. Simulation examples are presented.
A GPR for detection of buried cables and pipes was developed. The result is a 'lawn mower' model including antennas, electronics and on-line dataprocessing. Tests made for different targets showed reliable de...
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A GPR for detection of buried cables and pipes was developed. The result is a 'lawn mower' model including antennas, electronics and on-line dataprocessing. Tests made for different targets showed reliable detection even when the conditions are difficult.
In this paper, we derive some of the stochastic properties of a universal linear predictor, through analyses similar to those generally made in the adaptive signalprocessing literature. A. C. Singer et al. (see IEEE ...
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In this paper we show that there is measurable information in the articulatory system which can help to disambiguate the acoustic signal. We measure directly the movement of the lips, tongue, jaw, velum and larynx and...
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ISBN:
(纸本)7801501144
In this paper we show that there is measurable information in the articulatory system which can help to disambiguate the acoustic signal. We measure directly the movement of the lips, tongue, jaw, velum and larynx and parameterise this articulatory feature space using principal components analysis. The parameterisation is developed and evaluated using a speaker dependent phone recognition task on a specially recorded TIMIT corpus of 460 sentences. The results show that there is useful supplementary information contained in the articulatory data which yields a small but significant improvement in phone recognition accuracy of 2%. However, preliminary attempts to estimate the articulatory data from the acoustic signal and use this to supplement the acoustic input have not yielded any significant improvement in phone accuracy.
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