Infrared sensors and advanced signalprocessing are used to detect small (or "point") targets in highly cluttered and noisy environments. In this paper, a wavelet detection algorithm and tracking of small ta...
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ISBN:
(纸本)0819444782
Infrared sensors and advanced signalprocessing are used to detect small (or "point") targets in highly cluttered and noisy environments. In this paper, a wavelet detection algorithm and tracking of smalltargets in clutter will be discussed. A new registration algorithm based on optical flow estimates with matched subspace detectors against small maneuverable targets is also discussed. Both detectors incorporate adaptive constant false alarm rate (CFAR) detection statistics. Simulation of the detection and tracking algorithms using an unclassified database with a helicopter target and platform for the video cameras is summarized.
In recent years the first author has developed a unified, computationally tractable approach to multisensor-multitarget sensor management. This approach consists of closed-loop recursion of a PHD or CPHD filter with m...
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ISBN:
(纸本)9780819481627
In recent years the first author has developed a unified, computationally tractable approach to multisensor-multitarget sensor management. This approach consists of closed-loop recursion of a PHD or CPHD filter with maximization of a "natural" sensor management objective function called PENT (posterior expected number of targets). In this paper we extend this approach so that it can be used in unknown, dynamic clutter backgrounds.
This paper analyzes a new method to detect targets. The new method, called `super noncoherent integration' (SNCI), can improve overall detection performance by typically 5 dB to 10 dB relative to conventional nonc...
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ISBN:
(纸本)0819415391
This paper analyzes a new method to detect targets. The new method, called `super noncoherent integration' (SNCI), can improve overall detection performance by typically 5 dB to 10 dB relative to conventional noncoherent integration. A simple back-of-the-envelope formula is derived which quantifies the performance improvement of SNCI. Conventional noncoherent integration (CNCI) uses only amplitude measurements to distinguish targets from noise or clutter. In contrast, SNCI uses amplitude data in addition to: monopulse data, quadrature monopulse data, range and Doppler data over a sequence of N transmitted radar waveforms.
With multiple radar targets the sidelobe of matched filtering outputs from the stronger targets become the interferences for the detection of weaker targets. An effective approach based on traditional CLEAN algorithm ...
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ISBN:
(纸本)0819450774
With multiple radar targets the sidelobe of matched filtering outputs from the stronger targets become the interferences for the detection of weaker targets. An effective approach based on traditional CLEAN algorithm is proposed for sidelobe reduction for the matched filtering output of binary coding radar signals. A novel multilevel sidelobe reduction method is also developed for closely located multiple targets of nearly equally strong scatterings. The effectiveness of the sidelobe reduction algorithms is demonstrated through the numerous processing results.
We define the notion of an "identity variance" for expressing the level of uncertainty between target identities in a PDF representing the states of multiple targets. This, coupled with an OSPA covariance in...
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ISBN:
(纸本)9780819487476
We define the notion of an "identity variance" for expressing the level of uncertainty between target identities in a PDF representing the states of multiple targets. This, coupled with an OSPA covariance introduced in past work, can form a basis for evaluating both the accuracy of the state estimates as well as the confidence in the identities of the states. A potential application of the identity variance is as a criterion for choosing between waveforms optimized for producing accurate position estimates versus good classification information on a multifunction radar.
Because vehicles moving over ground generate a succession of impacts, the soil disturbances propagate away from the source as seismic waves. Thus, in the battlefield environment, we can detect moving ground vehicles b...
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Because vehicles moving over ground generate a succession of impacts, the soil disturbances propagate away from the source as seismic waves. Thus, in the battlefield environment, we can detect moving ground vehicles by means of measuring seismic signals using a seismic velocity transducer, and automatically classify and recognize them by advance signalprocessing method. Because seismic sensor is easy to be developed by emerging micro-electro-mechanical system (MEMS) technology, seismic detection that will be low cost, low power, small volume and light weight is a promising method for moving ground targets. Such a detection method can be used in many different fields, such as battlefield surveillance. traffic monitoring, law enforcement and so on. The paper researches seismic signals of typical vehicle targets in order to extract features of seismic signal and to recognize targets. As a data fusion method, the technique of artificial neural networks (ANN) is applied to recognize seismic signals for vehicle targets. An improved BP algorithm and ANN data fusion architecture have been presented to improve learning speed and avoid local minimum points in error curve. The algorithm had been used for classification and recognition of seismic signals of vehicle targets in the outdoor environment. It can be proven that moving ground vehicles can be detected by measuring seismic signal, feature extraction of target seismic signal is correct and ANN data fusion is effective to solve the recognition and classification problem for moving ground targets. (C) 2004 Elsevier Ltd. All rights reserved.
Distributed Sensor Concept - DISCO was proposed [I] for multiplication of individual sensor capabilities through non-coherent cooperative target engagement. The signalprocessing technique for DISCO is Recursive Adapt...
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ISBN:
(纸本)9780819471604
Distributed Sensor Concept - DISCO was proposed [I] for multiplication of individual sensor capabilities through non-coherent cooperative target engagement. The signalprocessing technique for DISCO is Recursive Adaptive Frame Integration of Limited data - RAFIL technique [2] that was initially proposed as a way to improve the SNR [3], reduce data rate [4] and mitigate FPA noise for IR sensors [5]. In DISCO, the RAFIL technique is used in a segmented way, when constituencies of the technique are spatially and temporally separated between individual sensors. Each sensor provides to and receives data from other sensors in the network. In this paper efficiency of DISCO is discussed for acquisition, accurate handover and track correlation of smalltargets.
Resolution limitations have a significant impact on accuracy of small target parameters estimated from imagery. This paper describes a model-based method for small target parameter estimation. The method requires an a...
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ISBN:
(纸本)0819428221
Resolution limitations have a significant impact on accuracy of small target parameters estimated from imagery. This paper describes a model-based method for small target parameter estimation. The method requires an a priori model of the target, and under many conditions will provide better results than deconvolution relying on pixel data alone. A description of the algorithm will be given, along with examples illustrating performance in a variety of situations, including a demonstration of the ability to handle some degree of saturation in the imaging process.
作者：
Chen, VCUSN
Res Lab Div Radar Washington DC 20375 USA
In this paper, we analyze features of radar returns from moving targets, introduce the basic concept of time-frequency-Radon transforms, describe the Radon transform for line feature detection, discuss their applicati...
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ISBN:
(纸本)0819444782
In this paper, we analyze features of radar returns from moving targets, introduce the basic concept of time-frequency-Radon transforms, describe the Radon transform for line feature detection, discuss their applications to detection of multiple moving targets in clutter, and demonstrate two examples of moving target detection using simulated radar data.
Classically, sensor signalprocessing and dataprocessing (i.e., tracking) have been performed separately with very little interaction between the two functions. Furthermore, the signalprocessing and tracking algorit...
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ISBN:
(纸本)0819415391
Classically, sensor signalprocessing and dataprocessing (i.e., tracking) have been performed separately with very little interaction between the two functions. Furthermore, the signalprocessing and tracking algorithms are often designed separately. This paper discusses some concepts for integrating the signalprocessing and tracking functions for a phase array radar. Since phased array radars provide a rapid beam steering capability, proper control of the radar beam has the potential for significantly improving the tracking of multiple maneuvering targets. However, when the signalprocessing is accomplished separately from the tracking, optimizing the detection thresholds for targets with fluctuating radar cross sections, resolving multiple targets, and reducing the errors due to multipath and glint must be accomplished over a single radar dwell period. Integrating the signalprocessing with the tracking will allow many of these issues to be addressed over multiple radar dwells. The issues associated with integrating the signalprocessing and tracking functions are discussed with respect to tracking and data association, revisit time and waveform energy calculations, and waveform selection. The waveform selection is discussed relative to four specific examples that include a fluctuating radar cross section from an extended target, two closely spaced targets, a splitting target, and a target in the presence of radar multipath.
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