Abstract:For known signals that are linearly superimposed on gaussian backgrounds, the linear adaptive matched filter (AMF) is well-known to be the optimal detector. The AMF has furthermore proved to be remarkably effective in a broad range of circumstances where it is not optimal, and for which the optimal detector is not linear. In these cases, nonlinear detectors are theoretically superior, but direct estimation of nonlinear detectors in highdimensional spaces often leads to flagrant overfitting and poor out-of-samp… Show more
“…Although this approach may provide some additional insight to existing detection algorithms, the resulting algorithms cannot be easily applied to practical applications. A nice discussion of several extensions to classical detection algorithms is given in 31 and the provided references.…”
Section: Mixtures Of Gaussian Distributionsmentioning
confidence: 99%
“…The quadratic detector is reduced to the linear matched filter (31) if Σ t = Σ b or if the observations are described by the additive signal model (39). The additive signal model assumes a fixed target with known shape and unknown amplitude.…”
Section: Detection Algorithms For Additive Subspace Models Of Spectramentioning
A large number of hyperspectral detection algorithms have been developed and used over the last two decades. Some algorithms are based on highly sophisticated mathematical models and methods; others are derived using intuition and simple geometrical concepts. The purpose of this paper is threefold. First, we discuss the key issues involved in the design and evaluation of detection algorithms for hyperspectral imaging data. Second, we present a critical review of existing detection algorithms for practical hyperspectral imaging applications. Finally, we argue that the "apparent" superiority of sophisticated algorithms with simulated data or in laboratory conditions, does not necessarily translate to superiority in real-world applications.
“…Although this approach may provide some additional insight to existing detection algorithms, the resulting algorithms cannot be easily applied to practical applications. A nice discussion of several extensions to classical detection algorithms is given in 31 and the provided references.…”
Section: Mixtures Of Gaussian Distributionsmentioning
confidence: 99%
“…The quadratic detector is reduced to the linear matched filter (31) if Σ t = Σ b or if the observations are described by the additive signal model (39). The additive signal model assumes a fixed target with known shape and unknown amplitude.…”
Section: Detection Algorithms For Additive Subspace Models Of Spectramentioning
A large number of hyperspectral detection algorithms have been developed and used over the last two decades. Some algorithms are based on highly sophisticated mathematical models and methods; others are derived using intuition and simple geometrical concepts. The purpose of this paper is threefold. First, we discuss the key issues involved in the design and evaluation of detection algorithms for hyperspectral imaging data. Second, we present a critical review of existing detection algorithms for practical hyperspectral imaging applications. Finally, we argue that the "apparent" superiority of sophisticated algorithms with simulated data or in laboratory conditions, does not necessarily translate to superiority in real-world applications.
“…There are several ways to arrive at this expression [19]. One method is to maximize a signal-to-clutter ratio much like the one maximized for Fisher's discriminant.…”
Section: Methods Of Detection and Identification Chemical Agentsmentioning
confidence: 99%
“…Though there are many paths to consider, there are only a few that significantly contribute to the signal measured at the sensor in LWIR measurements. Only those important paths will be considered in this development, which is drawn mainly from the literature focusing on the detection problem, but also from some work done on artificial plume insertion [18,19,[21][22][23]. Firstly, a model will be developed for paths that arrive at a sensor which is pointed at the ground, but not viewing any gaseous plumes in the atmosphere.…”
Section: Methods Of Detection and Identification Chemical Agentsmentioning
confidence: 99%
“…An operation range of the interferometer is 3-5 m (MWIR version) or 8-12 m (LWIR) and tuning velocity is of about [10][11][12][13][14][15][16][17][18][19][20] ms. An interferometer module, containing a detector matrix, has been developed by the Physical Sciences, Inc. and it is known as AIRIS (Adaptive InfraRed Imaging Spectroradiometer) [2].…”
Section: Passive Systems and Devices For Stand-off Gas Detectionmentioning
The paper presents a review of optoelectronic systems for stand-off detection of gases. Selected passive devices are presented, including thermal cameras with modified optics for remote gas detection and imaging Fourier transform spectrometers. The latter ones are capable of remote measurements of spectral characteristics of chemical compounds and, after further data analysis, they provide gas identification capabilities and even concentration estimates. This paper presents detection and identification of gases using an infrared imaging Fourier-transform spectrometer. The principle of operation of the spectrometer and the method for gases detection and identification is shown in the paper. The new software with implementation of method based on Clutter Match Filter, focused on detection and identification of gases is presented. Some results of the detection of various types of gases are also given.
The matched filter is an optimal linear filter for maximizing signal-to-noise ratio (SNR) in the presence of additive random noise. Learning-based matched filter is the proposed work. The proposed model supports (i) maximizing the SNR and (ii) setting the threshold, it is also called an intelligent matched dual tire filter. This improves the SNR by about 20 dB and the corresponding data rate to over 400 MBit/s. The bit error rate for 20 dB is very lower than FEC (10 −3 ) of up to 10 −7 . The accuracy of the proposed matched learning filter is proved to be above 95%. Indoor visible light communication is one of the applications, and it can also be used for image processing and outdoor communication such as satellite and radar communication. It is obvious for communication as it avoids signal degradation, lobing, and overfitting to achieve a better data rate. This requires funding support and infrastructure facility that can be used to deploy the proposed algorithm in the real-time scenario in the future.
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