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检索条件"任意字段=Conference on Signal and Data Processing of Small Targets 2000"

共 865 条记录，以下是221-230 订阅

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A Combined STAP/DPCA Algorithm for Enhanced Endoclutter Target Detection

A Combined STAP/DPCA Algorithm for Enhanced Endoclutter Targ...

引用

conference on Radar Sensor Technology XVIII

作者： Medl, Thomas Gen Atom Aeronaut Syst Inc GA ASI San Diego CA 92127 USA

ISBN: (纸本)9781628410143

Displaced Phase Center Antenna (DPCA) and Space-Time Adaptive processing (STA-P) are two general methods to cancel clutter in order to detect small, slowly moving targets that may be obscured by clutter. To detect these targets, the radar detection threshold needs to be as low as possible to ensure some minimum probability of detection (Pd). Unfortunately, lowering the radar threshold naturally results in a higher false alarm rate. Although there are standard methods such as M of N to reduce the false alarms, new techniques can potentially drive the false alarm rate down even further. Many "theoretical" papers have shown that STAP can be designed to outperform DPCA because of its potential additional "degrees-of-freedom". However, in "practice," this isn't always the case. For example, it is well known that STAP can have training issues in heterogeneous clutter. Typically, a radar signal processor will implement one method or the other to detect these small endoclutter targets. The technique being explored here is a two-fold approach in which the existing STAP code first processes the data in order to find a list of candidate targets. Next, a DPCA technique is also used to find a separate list of candidate detections from the same data. Although the algorithms are working on the same data, the processing is "independent" between them so the target lists are different. After both techniques have finished processing, the modified radar signal processing code "intelligently" combines the two detection lists into a single detection list. It will be shown that the combined list of detections from the two methods results in better detection performance than either method used separately.

关键词： endoclutter detection clutter suppression STAP DPCA SAR MTI data mining Weka

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IMM Filtering on Parametric data for Multi-Sensor Fusion

IMM Filtering on Parametric Data for Multi-Sensor Fusion

引用

conference on signal and data processing of small targets

作者： Shafer, Scott Owen, Mark SPAWAR Syst Ctr Pacific San Diego CA USA

ISBN: (纸本)9781628410297

In tracking, many types of sensor data can be obtained and utilized to distinguish a particular target. Commonly, kinematic information is used for tracking, but this can be combined with identification attributes and parametric information passively collected from the targets emitters. Along with the standard tracking process (predict, associate, score, update, and initiate) that operates in all kinematic trackers, parametric data can also be utilized to perform these steps and provide a means for feature fusion. Feature fusion, utilizing parametrics from multiple sources, yields a rich data set providing many degrees of freedom to separate and correlate data into appropriate tracks. Parametric radar data can take on many dynamics to include: stable, agile, jitter, and others. By utilizing a running sample mean and sample variance a good estimate of radar parametrics is achieved. However, when dynamics are involved, a severe lag can occur and a non-optimal estimate is achieved. This estimate can yield incorrect associations in feature space and cause track fragmentation or miscorrelation. In this paper we investigate the accuracy of the interacting multiple model (IMM) filter at estimating the first and second moments of radar parametrics. The algorithm is assessed by Monte Carlo simulation and compared against a running sample mean/variance technique. We find that the IMM approach yields a better result due to its ability to quickly adapt to dynamical systems with the proper model and tuning.

关键词： Interacting Multiple Model IMM sensor fusion radar pulse repetition interval pulse width RF SR

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small TARGET DETECTION BASED ON THREE-DIMENSIONAL PRINCIPAL COMPONENT ANALYSIS IN HYPERSPECTRAL IMAGERY 20

SMALL TARGET DETECTION BASED ON THREE-DIMENSIONAL PRINCIPAL ...

引用

conference on Image and signal processing for Remote Sensing XX

作者： Zhang, Xing Wen, Gongjian Natl Univ Def Technol ATR Lab Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781628413076

Research on target detection in hyperspectral imagery (HSI) has drawn much attention recently in many areas. Due to the limitation of the HSI sensor's spatial resolution, the target of interest normally occupies only a few pixels, sometimes are even present as subpixels. This may increase the difficulties in target detection. Moreover, in some cases, such as in the rescue and surveillance tasks, small targets are the most significant information. Therefore, it is very difficult but important to effectively detect the interested small target. Using a three-dimensional tensor to model an HSI data cube can preserve as many as possible the original spatial-spectral constraint structures, which is conducive to utilize the whole information for small target detection. This paper proposes a novel and effective algorithm for small target detection in HSI based on three-dimensional principal component analysis (3D-PCA). According to the 3D-PCA, the significant components usually contain most information of imagery, in contrast, the details of small targets exist in the insignificant components. So, after 3D-PCA implemented on the HSI, the significant components which indicate the background of HSI are removed and the insignificant components are used to detect small targets. The algorithm is outstanding thanks to the tensor-based method which is applied to process the HSI directly, making full use of spatial and spectral information, by employing multilinear algebra. Experiments with a real HSI show that the detection probability of interested small targets improved greatly compared to the classical RX detector.

关键词： Hyperspectral imagery (HSI) small target detection tensor representation 3D-PCA multilinear algebra

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Bias Estimation for Moving Optical Sensor Measurements with targets of Opportunity

Bias Estimation for Moving Optical Sensor Measurements with ...

引用

conference on signal and data processing of small targets

作者： Belfadel, Djedjiga Osborne, Richard W., III Bar-Shalom, Yaakov Univ Connecticut Dept Elect & Comp Engn Storrs CT 06269 USA

ISBN: (纸本)9781628410297

Integration of space based sensors into a Ballistic Missile Defense System (BMDS) allows for detection and tracking of threats over a larger area than ground based sensors [1]. This paper examines the effect of sensor bias error on the tracking quality of a Space Tracking and Surveillance System (STSS) for the highly non-linear problem of tracking a ballistic missile. The STSS constellation consists of two or more satellites (on known trajectories) for tracking ballistic targets. Each satellite is equipped with an IR sensor that provides azimuth and elevation to the target. The tracking problem is made more difficult due to a constant or slowly varying bias error present in each sensor's line of sight measurements. It is important to correct for these bias errors so that the multiple sensor measurements and/or tracks can be referenced as accurately as possible to a common tracking coordinate system. The measurements provided by these sensors are assumed time-coincident (synchronous) and perfectly associated. The line of sight (LOS) measurements from the sensors can be fused into measurements which are the Cartesian target position, i.e., linear in the target state. We evaluate the Cramer-Rao Lower Bound (CRLB) on the covariance of the bias estimates, which serves as a quantification of the available information about the biases. Statistical tests on the results of simulations show that this method is statistically efficient, even for small sample sizes (as few as two sensors and six points on the (unknown) trajectory of a single target of opportunity). We also show that the RMS position error is significantly improved with bias estimation compared with the target position estimation using the original biased measurements.

关键词： Bias estimation space tracking observability statistical efficiency CRLB composite measurements maximum likelihood

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Estimating moving target information using single-channel synthetic aperture radar (SAR)

Estimating moving target information using single-channel sy...

引用

conference on Algorithms for Synthetic Aperture Radar Imagery XXI

作者： Gunther, Jake Hunsaker, Josh Anderson, Hyrum Moon, Todd Utah State Univ Logan UT 84322 USA Sandia Natl Labs Data Anal & Data Exploitat Albuquerque NM USA

ISBN: (纸本)9781628410303

Simultaneously estimating position x and velocity v of moving targets using only the measured phase phi from single-channel SAR is impossible because the mapping from (x, v) to phi is many-to-one. This paper defines classes of equivalent target motion and solves the GMTI problem up to membership in an equivalence class using single-channel SAR phase data. Definitions are presented for endo-and exo-clutter that are consistent with the equivalence classes, and it is shown that most target motion can be detected, i.e. the set of endo-clutter targets is very small. We exploit the sparsity of moving targets in the scene to develop an algorithm to resolve target motion up to membership in an equivalence class, and demonstrate the effectiveness of the proposed technique using simulated data.

关键词： R adar signal processing synthetic aperture radar (SAR) ground moving target indicators (GMTI) motion detection sparse signal recovery orthogonal matching pursuits (OMP)

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Study of data fusion algorithms applied to unattended ground sensor network

Study of data fusion algorithms applied to unattended ground...

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conference on signal processing, Sensor/Information Fusion, and Target Recognition XXIII

作者： Pannetier, B. Moras, J. Dezert, J. Sella, G. Off Natl Etud & Rech Aerosp Informat Proc & Modelling Dept F-91761 Palaiseau France SAGEM Def & Opt Div CED CV F-91311 Massy France

ISBN: (纸本)9781628410280

In this paper, data obtained from wireless unattended ground sensor network are used for tracking multiple ground targets (vehicles, pedestrians and animals) moving on and off the road network. The goal of the study is to evaluate several data fusion algorithms to select the best approach to establish the tactical situational awareness. The ground sensor network is composed of heterogeneous sensors (optronic, radar, seismic, acoustic, magnetic sensors) and data fusion nodes. The fusion nodes are small hardware platforms placed on the surveillance area that communicate together. In order to satisfy operational needs and the limited communication bandwidth between the nodes, we study several data fusion algorithms to track and classify targets in real time. A multiple targets tracking (MTT) algorithm is integrated in each data fusion node taking into account embedded constraint. The choice of the MIT algorithm is motivated by the limit of the chosen technology. In the fusion nodes, the distributed MTT algorithm exploits the road network information in order to constrain the multiple dynamic models. Then, a variable structure interacting multiple model (VS-IMM) is adapted with the road network topology. This algorithm is well-known in centralized architecture, but it implies a modification of other data fusion algorithms to preserve the performances of the tracking under constraints. Based on such VS-IMM MIT algorithm, we adapt classical data fusion techniques to make it working in three architectures: centralized, distributed and hierarchical. The sensors measurements are considered asynchronous, but the fusion steps are synchronized on all sensors. Performances of data fusion algorithms are evaluated using simulated data and also validated on real data. The scenarios under analysis contain multiple targets with close and crossing trajectories involving data association uncertainties.

关键词： Multiple target tracking wireless sensor network data fusion architecture

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Kalman-Based Tracker for Multiple Radar targets

Kalman-Based Tracker for Multiple Radar Targets

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10th International conference on Communications (COMM)

作者： Macaveiu, Adrian Campeanu, Andrei Nafornita, Ioan Politehn Univ Timisoara Commun Dept Timisoara Romania

ISBN: (纸本)9781479923854

Automotive radars are used for driver assistance systems such as Cruise Control, Lane Change Assistant or Vulnerable Road User (VRU) protection. In all these applications the signal processing architecture must consider target detection, data association and tracking for the proper analysis of the realistic traffic situation and for applying the correct safety measures. This paper proposes a data association and tracking algorithm based on the Hungarian association procedure and Kalman filtering. The simulations show that this scheme can be used in multiple target situations.

关键词： tracking Kalman filter data association automotive radar

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Shallow depth subsurface imaging with microwave holography

Shallow depth subsurface imaging with microwave holography

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conference on Detection and Sensing of Mines, Explosive Objects, and Obscured targets XIX

作者： Zhuravlev, Andrei Ivashov, Sergey Razevig, Vladimir Vasiliev, Igor Bechtel, Timothy Bauman Moscow State Tech Univ Remote Sensing Lab 5 2nd Baumanskaya Moscow 105005 Russia Franklin & Marshall Coll Dept Earth & Environm Lancaster PA 17604 USA

ISBN: (纸本)9781628410099

In this paper, microwave holography is considered as a tool to obtain high resolution images of shallowly buried objects. signal acquisition is performed at multiple frequencies on a grid using a two-dimensional mechanical scanner moving a single transceiver over an area of interest in close proximity to the surface. The described FFT-based reconstruction technique is used to obtain a stack of plan view images each using only one selected frequency from the operating waveband of the radar. The extent of a synthetically-formed aperture and the signal wavelength define the plan view resolution, which at sounding frequencies near 7 GHz amounts to 2 cm. The system has a short depth of focus which allows easy selection of proper focusing plane. The small distance from the buried objects to the antenna does not prevent recording of clean images due to multiple reflections (as happens with impulse radars). The description of the system hardware and signal processing technique is illustrated using experiments conducted in dry sand. The microwave images of inert anti-personnel mines are demonstrated as examples. The images allow target discrimination based on the same visually-discernible small features that a human observer would employ. The demonstrated technology shows promise for modification to meet the specific practical needs required for humanitarian demining or in multi-sensor survey systems.

关键词： Microwave holography holographic subsurface radar humanitarian demining synthetic aperture radar microwave imaging automatic data acquisition linear actuator system

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Deep Learning Algorithms for Detecting Explosive Hazards in Ground Penetrating Radar data

Deep Learning Algorithms for Detecting Explosive Hazards in ...

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conference on Detection and Sensing of Mines, Explosive Objects, and Obscured targets XIX

作者： Besaw, Lance E. Stimac, Philip J. Appl Res Associates Randolph VT 05060 USA

ISBN: (纸本)9781628410099

Buried explosive hazards (BEHs) have been, and continue to be, one of the most deadly threats in modern conflicts. Current handheld sensors rely on a highly trained operator for them to be effective in detecting BEHs. New algorithms are needed to reduce the burden on the operator and improve the performance of handheld BEH detectors. Traditional anomaly detection and discrimination algorithms use "hand-engineered" feature extraction techniques to characterize and classify threats. In this work we use a Deep Belief Network (DBN) to transcend the traditional approaches of BEH detection (e. g., principal component analysis and real-time novelty detection techniques). DBNs are pretrained using an unsupervised learning algorithm to generate compressed representations of unlabeled input data and form feature detectors. They are then fine-tuned using a supervised learning algorithm to form a predictive model. Using ground penetrating radar (GPR) data collected by a robotic cart swinging a handheld detector, our research demonstrates that relatively small DBNs can learn to model GPR background signals and detect BEHs with an acceptable false alarm rate (FAR). In this work, our DBNs achieved 91% probability of detection (P-d) with 1.4 false alarms per square meter when evaluated on anti-tank and anti-personnel targets at temperate and arid test sites. This research demonstrates that DBNs are a viable approach to detect and classify BEHs.

关键词： Anomaly detection ground penetrating radar neural networks deep learning unsupervised learning buried explosive hazard signal processing

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The bottom reverberation suppression algorithm for Side Scan Sonar

The bottom reverberation suppression algorithm for Side Scan...

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Oceans conference

作者： Liu Jia Xu Feng Yang Juan An Xudong Chinese Acad Sci Inst Acoust Beijing Peoples R China

ISBN: (纸本)9781479949182

The sea bottom reverberation is one of the important problems, which restrict the bottom targets detection performance with the high frequency imaging sonar. In this paper, a bottom reverberation suppression algorithm is presented to improve the performance of the high frequency side scan sonar for small target detection. In the paper, the statistics characteristic of the target echo and the bottom reverberation is introduced, with the experiment data processing. The target echo and the bottom reverberation could be separated with the difference of the statistics characteristic. And the Blind Source Separated method was applied to implement the signal separation. At last, the effective of this method was proved by the process of the experiment data.

关键词： Side scan sonar small target detection Bottom reverberation suppressio Reverberation statistics characteristic Blind Source Separated

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