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Relevant bibliographies by topics / Tracking radar Data processing

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Academic literature on the topic 'Tracking radar Data processing'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Tracking radar Data processing"

1

Hero, A. "Radar data processing: Vol. I - Introduction and tracking." IEEE Transactions on Acoustics, Speech, and Signal Processing 34, no. 5 (October 1986): 1350–51. http://dx.doi.org/10.1109/tassp.1986.1164939.

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Morgera, Salvatore D. "Radar data processing — volume I: Introduction and tracking." Signal Processing 11, no. 4 (December 1986): 413–15. http://dx.doi.org/10.1016/0165-1684(86)90084-8.

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Kim, Beom-Hun, Seung-Jo Han, Goo-Rak Kwon, and Jae-Young Pyun. "Signal Processing for Tracking of Moving Object in Multi-Impulse Radar Network System." International Journal of Distributed Sensor Networks 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/536841.

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Indoor positioning systems (IPSs) have been discussed for use in entertainment, home automation, rescue, surveillance, and healthcare applications. In this paper, we present an IPS that uses an impulse radio-ultra-wideband (IR-UWB) radar network. This radar network system requires at least two radar devices to determine the current coordinates of a moving person. However, one can enlarge the monitoring area by adding more radar sensors. To track moving targets in indoor environments, for example, patients in hospitals or intruders in a home, signal processing procedures for tracking should be applied to the raw data measured using IR-UWB radars. This paper presents the signal processing method required for robust target tracking in a radar network, that is, an iterative extended Kalman filter- (IEKF-) based object tracking method, which uses two IR-UWB radars to measure the coordinates of the targets. The proposed IEKF tracking method is compared to the conventional extended Kalman filter (EKF) method. The results verify that the IEKF method improves the performance of 2D target tracking in a real-time system.

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Chen, Weishi. "Interactive processing of radar target detection and tracking." Aircraft Engineering and Aerospace Technology 90, no. 9 (November 14, 2018): 1337–45. http://dx.doi.org/10.1108/aeat-07-2016-0115.

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Purpose An interactive processing scheme is proposed to improve the target detection probability as well as the tracking performance of the radar system. Design/methodology/approach Firstly, with the spatial-correlated features extracted from the foreground and background statistical models, the thresholds were adapted to distinguish the dim small targets from clutters in the complex incoherent radar images. Then, the target trajectories were constructed with the target tracking algorithm. According to the temporal correlation with the target life cycle, the thresholding values were modified in the neighbourhood of the predicted positions to improve the detection sensitivity in these areas during the tracking process. Finally, the temporal-correlated features of the remained clutters were used to further reduce the false alarm rate. Findings The proposed algorithm was applied on the simulated data, as well as the image sequences obtained with the incoherent marine radars. The detection results demonstrated that the interactive algorithm could detect and track the dim small targets with relatively low false alarm rate. Practical implications The interactive processing scheme could be applied for low-altitude airspace surveillance with incoherent marine radar. Originality/value The proposed scheme outperforms the classical radar target detection algorithms and the state-of-the-art image processing algorithms for video-based surveillance.

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Bureneva, O. I., I. G. Gorbunov, G. V. Komarov, A. A. Konovalov, M. S. Kupriyanov, and Yu A. Shichkina. "A Prototype of Automotive 77 GHz Radar." Journal of the Russian Universities. Radioelectronics 24, no. 3 (June 24, 2021): 22–38. http://dx.doi.org/10.32603/1993-8985-2021-24-3-22-38.

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Introduction. Automotive radars are the main tools for providing traffic safety. The development of such radars involve a number of technical difficulties due to the manufacture of high-precision extremely high-frequency (EHF) printed circuit boards. To facilitate the process of creating such devices, the existing algorithms for radar information processing should be debugged using prototypes from manufacturers of mm-band transceivers. However, the parameters of such boards are not known in advance, and the actual operating conditions of the as-produced automotive radars raise new challenges to target tracking algorithms. Therefore, checking the performance of such boards is a relevant research problem.Aim. To evaluate the performance of a millimeter-wave automotive radar prototype and to test target tracking algorithms using this prototype.Materials and methods. An original target tracking method was used, which considers the constraints on the use of additional data sources about the radar carrier movement.Results. An experimental performance evaluation of a 77 GHz automotive radar prototype was carried out. The effectiveness of primary processing for the target class “vehicle” in the millimetre range was checked. Original algorithms for target tracking were proposed and tested.Conclusion. The obtained results show that the prototype board of a transceiver chip is capable of testing tracking algorithms without creating an own automotive radar prototype. Thus, the developmental process can be significantly shortened. Moreover, after creating a hardware solution, the developer obtains a reference device to test and configure an own product without using extremely expensive and rare EHF equipment.

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Palamarchuk, Yu O., S. V. Ivanov, and I. G. Ruban. "The digitizing algorithm for precipitation in the atmosphere on the base of radar measurements." Ukrainian hydrometeorological journal, no. 18 (October 29, 2017): 40–47. http://dx.doi.org/10.31481/uhmj.18.2016.05.

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There is an increasing demand for automated high-quality very-short-range forecasts and nowcasts of precipitation on small scales and at high update frequencies. Current prediction systems use different methods of determining precipitation such as area tracking, individual cell tracking and numerical models. All approaches are based on radar measurements. World-leading manufactories of meteorological radars and attendant visualization software are introduced in the paper. Advantages of the numerical modelling against inertial schemes designed on statistical characteristics of convective processes are outlined. On this way, radar data assimilation systems as a necessary part of numerical models are intensively developed. In response to it, the use of digital formats for processing of radar measurements in numerical algorithms became important. In the focus of this work is the developing of a unified code for digital processing of radar signals at the preprocessing, filtration, assimilation and numerical integration steps. The proposed code also includes thinning, screening or superobbing radar data before exploring them for the assimilation procedures. The informational model manages radar data flows in the metadata and binary array forms. The model constitutes an official second-generation European standard exchange format for weather radar datasets from different manufactories. Results of radar measurement processing are presented for both, the single radar and radar overlying network.

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Daliakopoulos, Ioannis N., and Ioannis K. Tsanis. "A weather radar data processing module for storm analysis." Journal of Hydroinformatics 14, no. 2 (July 14, 2011): 332–44. http://dx.doi.org/10.2166/hydro.2011.118.

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A pre- and post-processing weather radar data module was developed in the Matlab suite of software with GIS data exchange abilities for storm event analysis. During pre-processing, each radar sweep is converted from spherical to Cartesian coordinates in the desired temporal and spatial resolution. The module's functionality in post processing includes radar data display, geo-referencing over GIS maps, data filtering with the Wiener filter and single or multiple sweep processing. The user can perform individual storm cell detection and tracking, resulting in the storm's average velocity and track length. The tested methods are modifications of the LoG (Laplacian of the Gaussian) blob detection method and a Brownian particle trajectory linking algorithm. Radar reflectivity factor (Z) data can be referenced over predefined rainfall (R) gauges in order to determine the radar Z–R equation parameters. The user can also produce spatially distributed precipitation estimates by using standard Z–R equations from the literature. The module's functionality is demonstrated using data from a rainfall event captured by the NSA Souda Bay C-Band radar during a storm in October 2006. Results show that the Rosenfeld Tropical Z–R equation is the one that gives a satisfactory description of the spatial and temporal precipitation distribution of the investigated event.

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Hong, Yong Bin, Cheng Fa Xu, Mei Guo Gao, and Li Zhi Zhao. "A High-Performance Signal Processing System for Monopulse Tracking Radar." Advanced Materials Research 383-390 (November 2011): 471–75. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.471.

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A radar signal processing system characterizing high instantaneous dynamic range and low system latency is designed based on a specifically developed signal processing platform. Instantaneous dynamic range loss is a critical problem when digital signal processing is performed on fixed-point FPGAs. In this paper, the problem is well resolved by increasing the wordlength according to signal-to-noise ratio (SNR) gain of the algorithms through the data path. The distinctive software structure featuring parallel pipelined processing and “data flow drive” reduces the system latency to one coherent processing interval (CPI), which significantly improves the maximum tracking angular velocity of the monopulse tracking radar. Additionally, some important electronic counter-countermeasures (ECCM) are incorporated into this signal processing system.

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Fiscante, Nicomino, Pia Addabbo, Carmine Clemente, Filippo Biondi, Gaetano Giunta, and Danilo Orlando. "A Track-Before-Detect Strategy Based on Sparse Data Processing for Air Surveillance Radar Applications." Remote Sensing 13, no. 4 (February 12, 2021): 662. http://dx.doi.org/10.3390/rs13040662.

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In this paper we consider the tracking problem of a moving target competing against noise and clutter in a surveillance radar scenario. For a single array-antenna multiple-target tracking system and according to the Track-Before-Detect paradigm, we present a novel approach based on a three-stage processing chain that involves the Sparse Learning via Iterative Minimization algorithm, the k-means clustering method and the ad hoc detector by exploiting the sparse nature of the operating scenario. Under the latter assumption, the detection strategy declares the presence of targets subsequently to the retrieval of their corresponding tracks performed by jointly processing the received echoes of multiple consecutive radar scans. Simulation results show that the proposed approach is able to provide good tracking and detection capabilities for different multiple target trajectories with low Signal-to-Interference-plus-Noise ratio and results in providing advantages when compared to a number of other reference Track-Before-Detect strategies based on sparse data processing techniques.

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Nikolic, Dejan, Nikola Stojkovic, Pavle Petrovic, Nikola Tosic, Nikola Lekic, Zoran Stankovic, and Nebojsa Doncov. "The high frequency surface wave radar solution for vessel tracking beyond the horizon." Facta universitatis - series: Electronics and Energetics 33, no. 1 (2020): 37–59. http://dx.doi.org/10.2298/fuee2001037n.

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With maximum range of about 200 nautical miles (approx. 370 km) High Frequency Surface Wave Radars (HFSWR) provide unique capability for vessel detection far beyond the horizon without utilization of any moving platforms. Such uniqueness requires design principles unlike those usually used in microwave radar. In this paper the key concepts of HFSWR based on Frequency Modulated Continuous (FMCW) principles are presented. The paper further describes operating principles with focus on signal processing techniques used to extract desired data. The signal processing describes range and Doppler processing but focus is given to the Digital Beamforming (DBF) and Constant False Alarm Rate (CFAR) models. In order to better present the design process, data obtained from the HFSWR sites operating in the Gulf of Guinea are used.

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Dissertations / Theses on the topic "Tracking radar Data processing"

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Filippidis, Arthur. "Multisensor data fusion." Title page, contents and abstract only, 1993. http://web4.library.adelaide.edu.au/theses/09ENS/09ensf482.pdf.

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Kiefer, Jessica L. "Target Tracking Using Various Filters in Synthetic Aperture Radar Data and Imagery." DigitalCommons@CalPoly, 2009. https://digitalcommons.calpoly.edu/theses/121.

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This thesis explores the use and accuracy of several discrete-time image filters for the purpose of target tracking in Synthetic Aperture Radar imagery. Both extended targets and point targets are used for tracking, showing the need for different types of filters for each target type. Monte Carlo analysis is performed on the results of the extended target filter results to determine the absolute mean-squared error between the filter prediction of the target centroid and the actual location of the target centroid. Two different filters were chosen for the extended target: Kalman and H Infinity. Both the Kalman and H Infinity filters perform tracking by accurately estimating the state of the dynamic system, and in some cases it may be useful to simulate a situation when a target temporarily disappears from radar view. The ability of both filters to predict target location with no input measurements is investigated. A unique trait of the H Infinity filter is its ability to accurately and efficiently estimate the state of a dynamic system given no information about the noise environment. To simulate more realistic targets, smaller circular and square targets are created and a sensitivity analysis is performed using the Kalman and H Infinity filters to determine the shortfalls of these filter techniques as targets become smaller and smaller. The results indicate that these tracking methods are no longer useful as the targets become so small that they approach being only a single pixel in size. A new filter called the Prediction and Matching Detection (PAMD) filter is used for single-pixel point targets. This filter illustrates the importance of having very high frame rate images with little change in velocity over consecutive frames if choosing to use the PAMD algorithm. The PAMD filter is extended to track more than one target at a time. Tracking of raw SAR data is preferred over post-processed images due to the decreased amount of processing time. The Kalman and H Infinity filters are implemented to track raw radar data during its first 3 seconds of motion in 2-dimensions by accounting for the measurements of two parameters: the squint angle and slant range. Noise is added to the measurements to simulate platform inaccuracies. The project is a continuation of prior SAR research at Cal Poly under Dr. John Saghri with the sponsorship of Raytheon Space & Airborne Systems.

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Jeong, Soonho Tugnait Jitendra K. "Topics in multisensor maneuvering target tracking." Auburn, Ala., 2005. http://repo.lib.auburn.edu/2005%20Summer/doctoral/JEONG_SOONHO_43.pdf.

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Brewster, Wayne Allan. "Space tether - radar data processing." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1994. http://handle.dtic.mil/100.2/ADA289654.

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Thesis (M.S. in Electrical Engineering and M.S. in Applied Physics) Naval Postgraduate School, September 1994.
Thesis advisor(s): Richard Christopher Olsen, Ralph Hippenstiel. "September 1994." Bibliography: p. 71. Also available online.

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Glass, John David. "Monopulse processing and tracking of maneuvering targets." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53556.

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As part of the processing of tracking targets, surveillance radars detect the presence of targets and estimate their locations. This dissertation re-examines some of the often ignored practical considerations of radar tracking. With the advent of digital computers, modern radars now use sampled versions of received signals for processing. Sampling rates used in practice result in the bin-straddling phenomenon, which is often treated as an undesired loss in signal power. Here, a signal model that explicitly models the sampling process is used in the derivation of the average loglikelihood ratio test (ALLRT), and its detection performance is shown to defeat the bin-straddling losses seen in traditional radar detectors. In monopulse systems, data samples are taken from the sum and difference channels, by which a target direction-of-arrival (DOA) estimate can be formed. Using the same signal model, we derive new estimators for target range, strength, and DOA and show performance benefits over traditional monopulse techniques that are predominant in practice. Since tracking algorithms require an error variance report on target parameter estimates, we propose using the generalized Cramer-Rao lower bound (GCRLB), which is the CRLB evaluated at estimates rather than true values, as an error variance report. We demonstrate the statistical efficiency and variance consistency of the new estimators. With several parameter estimates collected over time, tracking algorithms are used to compute track state estimates and predict future locations. Using agile- beam surveillance radars with programmable energy waveforms, optimal scheduling of radar resources is a topic of interest. In this dissertation, we focus on the energy management considerations of tracking highly maneuverable aircraft. A comparison between two competing interacting multiple model (IMM) filter configurations is made, and a recently proposed unbiased mixing procedure is extended to the case of three modes. Finally, we introduce the radar management operating curve (RMOC), which shows the fundamental tradeoff in radar time and energy, to aid radar designers in the selection of an overall operating signal-to-noise level.

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Aygar, Alper. "Doppler Radar Data Processing And Classification." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609890/index.pdf.

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In this thesis, improving the performance of the automatic recognition of the Doppler radar targets is studied. The radar used in this study is a ground-surveillance doppler radar. Target types are car, truck, bus, tank, helicopter, moving man and running man. The input of this thesis is the output of the real doppler radar signals which are normalized and preprocessed (TRP vectors: Target Recognition Pattern vectors) in the doctorate thesis by Erdogan (2002). TRP vectors are normalized and homogenized doppler radar target signals with respect to target speed, target aspect angle and target range. Some target classes have repetitions in time in their TRPs. By the use of these repetitions, improvement of the target type classification performance is studied. K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithms are used for doppler radar target classification and the results are evaluated. Before classification PCA (Principal Component Analysis), LDA (Linear Discriminant Analysis), NMF (Nonnegative Matrix Factorization) and ICA (Independent Component Analysis) are implemented and applied to normalized doppler radar signals for feature extraction and dimension reduction in an efficient way. These techniques transform the input vectors, which are the normalized doppler radar signals, to another space. The effects of the implementation of these feature extraction algoritms and the use of the repetitions in doppler radar target signals on the doppler radar target classification performance are studied.

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Hulot, Carlos. "Parallel tracking systems." Thesis, University of Southampton, 1995. https://eprints.soton.ac.uk/264882/.

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Tracking Systems provide an important analysis technique that can be used in many different areas of science. A Tracking System can be defined as the estimation of the dynamic state of moving objects based on `inaccurate’ measurements taken by sensors. The area encompasses a wide range of subjects, although the two most essential elements are estimation and data association. Tracking systems are applicable to relatively simple as well as more complex applications. These include air traffic control, ocean surveillance and control sonar tracking, military surveillance, missile guidance, physics particle experiments, global positioning systems and aerospace. This thesis describes an investigation into state-of-the-art tracking algorithms and distributed memory architectures (Multiple Instructions Multiple Data systems - “MIMD”) for parallel processing of tracking systems. The first algorithm investigated is the Interacting Multiple Model (IMM) which has been shown recently to be one of the most cost-effective in its class. IMM scalability is investigated for tracking single targets in a clean environment. Next, the IMM is coupled with a well-established Bayesian data association technique known as Probabilistic Data Association (PDA) to permit the tracking of a target in different clutter environments (IMMPDA). As in the previous case, IMMPDA scalability is investigated for tracking a single target in different clutter environments. In order to evaluate the effectiveness of these new parallel techniques, standard languages and parallel software systems (to provide message-passing facilities) have been used. The main objective is to demonstrate how these complex algorithms can benefit in the general case from being implemented using parallel architectures.

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Christiansen, Jonas Myhre. "Fully adaptive radar for detection and tracking." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1587093543249087.

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Mota, Gilberto Ferreira. "Radar data processing using a distributed computational system." Thesis, Monterey, California. Naval Postgraduate School, 1992. http://hdl.handle.net/10945/24022.

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This research specifies and validates a new concurrent decomposition scheme, called Confined Space Search Decomposition (CSSD), to exploit parallelism of Radar Data Processing algorithms using a Distributed Computational System. To formalize the specification we propose and apply an object-oriented methodology called Decomposition Cost Evaluation Model (DCEM). To reduce the penalties of load imbalance we propose a distributed dynamic load balance heuristic called Object Reincarnation (OR). To validate the research we first compare our decomposition with an identified alternative using the proposed DCEM model and then develop a theoretical prediction of selected parameters. We also develop a simulation to check the Object Reincarnation concept.

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Bostanudin, Nurul Jihan Farhah. "Computational methods for processing ground penetrating radar data." Thesis, University of Portsmouth, 2013. https://researchportal.port.ac.uk/portal/en/theses/computational-methods-for-processing-ground-penetrating-radar-data(d519f94f-04eb-42af-a504-a4c4275d51ae).html.

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The aim of this work was to investigate signal processing and analysis techniques for Ground Penetrating Radar (GPR) and its use in civil engineering and construction industry. GPR is the general term applied to techniques which employ radio waves, typically in the Mega Hertz and Giga Hertz range, to map structures and features buried in the ground or in manmade structures. GPR measurements can suffer from large amount of noise. This is primarily caused by interference from other radio-wave-emitting devices (e.g., cell phones, radios, etc.) that are present in the surrounding area of the GPR system during data collection. In addition to noise, presence of clutter – reflections from other non-target objects buried underground in the vicinity of the target can make GPR measurement difficult to understand and interpret, even for the skilled human, GPR analysts. This thesis is concerned with the improvements and processes that can be applied to GPR data in order to enhance target detection and characterisation process particularly with multivariate signal processing techniques. Those primarily include Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Both techniques have been investigated, implemented and compared regarding their abilities to separate the target originating signals from the noise and clutter type signals present in the data. Combination of PCA and ICA (SVDPICA) and two-dimensional PCA (2DPCA) are the specific approaches adopted and further developed in this work. Ability of those methods to reduce the amount of clutter and unwanted signals present in GPR data have been investigated and reported in this thesis, suggesting that their use in automated analysis of GPR images is a possibility. Further analysis carried out in this work concentrated on analysing the performance of developed multivariate signal processing techniques and at the same time investigating the possibility of identifying and characterising the features of interest in pre-processed GPR images. The driving idea behind this part of work was to extract the resonant modes present in the individual traces of each GPR image and to use properties of those poles to characterise target. Three related but different methods have been implemented and applied in this work – Extended Prony, Linear Prediction Singular Value Decomposition and Matrix Pencil methods. In addition to these approaches, PCA technique has been used to reduce dimensionality of extracted traces and to compare signals measured in various experimental setups. Performance analysis shows that Matrix Pencil offers the best results.

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Books on the topic "Tracking radar Data processing"

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Lei da ce liang yu ying yong. Beijing: Guo fang gong ye chu ban she, 2011.

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Information, Decision, and Control (2007 Adelaide, Aust.). 2007 Information Decision and Control (IDC): Adelaide, Australia, 12-14 February 2007. Piscataway, NJ: IEEE, 2007.

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Information, Decision, and Control (2007 Adelaide, Aust.). 2007 Information Decision and Control (IDC): Adelaide, Australia, 12-14 February 2007. Piscataway, NJ: IEEE, 2007.

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Information, Decision, and Control (2007 Adelaide, Aust.). 2007 Information Decision and Control (IDC): Adelaide, Australia, 12-14 February 2007. Piscataway, NJ: IEEE, 2007.

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Information, Decision, and Control (2007 Adelaide, Australia). 2007 Information Decision and Control (IDC): Adelaide, Australia, 12-14 February 2007. Piscataway, NJ: IEEE, 2007.

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Kadar, Ivan. Signal processing, sensor fusion, and target recognition XIX: 5-7 April 2010, Orlando, Florida, United States. Bellingham, Wash: SPIE, 2010.

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Australian Data Fusion Symposium (1st 1996 Adelaide, Australia). First Australian Data Fusion Symposium: ADFS-96 Adelaide, Australia, November 21-22, 1996. Piscataway, NJ: Institute of Electrical and Electronics Engineers, 1996.

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Australian, Data Fusion Symposium (1st 1996 Adelaide Australia). First Australian Data Fusion Symposium: November 21-22, 1996, Stamford Plaza Hotel, Adelaide, Australia. [New York?]: IEEE, 1996.

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A, Studer F., ed. Radar data processing. Letchworth, Hertfordshire, England: Research Studies Press, 1985.

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You, He, Xiu Jianjuan, and Guan Xin. Radar Data Processing with Applications. Singapore: John Wiley &;#38; Sons Singapore Pte. Ltd, 2016. http://dx.doi.org/10.1002/9781118956878.

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Book chapters on the topic "Tracking radar Data processing"

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Capraro, Gerard T., Alfonso Farina, Hugh D. Griffiths, and Michael C. Wicks. "Knowledge-Based Radar Signal and Data Processing: A Tutorial Overview." In Knowledge-Based Radar Detection, Tracking, and Classification, 31–53. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2007. http://dx.doi.org/10.1002/9780470283158.ch3.

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Kazimierski, Witold, and Grzegorz Zaniewicz. "Analysis of the Possibility of Using Radar Tracking Method Based on GRNN for Processing Sonar Spatial Data." In Rough Sets and Intelligent Systems Paradigms, 319–26. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08729-0_32.

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Gamba, Jonah. "Target Filtering and Tracking." In Radar Signal Processing for Autonomous Driving, 87–104. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9193-4_7.

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Fitch, J. Patrick. "Optical Processing of SAR Data." In Synthetic Aperture Radar, 85–108. New York, NY: Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4612-3822-5_3.

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Knott, Eugene F. "Data Processing and Reduction." In Radar Cross Section Measurements, 350–84. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4684-9904-9_9.

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Barrett, R. F., A. K. Steele, and R. L. Streit. "Frequency Line Tracking Algorithms." In Underwater Acoustic Data Processing, 497–501. Dordrecht: Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-009-2289-1_56.

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Economou, Nikos, Antonis Vafidis, Francesco Benedetto, and Amir M. Alani. "GPR Data Processing Techniques." In Civil Engineering Applications of Ground Penetrating Radar, 281–97. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-04813-0_11.

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Beringuer, Bernard, and Jean-Louis Maridat. "Castor Project Improved Processing of Radar Data in France." In Weather Radar Networking, 217–24. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0551-1_25.

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Koch, Wolfgang. "On Recursive Batch Processing." In Tracking and Sensor Data Fusion, 89–105. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39271-9_5.

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Wicks, Michael C., Muralidhar Rangaswamy, Raviraj S. Adve, and Todd B. Hale. "Space-Time Adaptive Processing for Airborne Radar: A Knowledge-Based Perspective." In Knowledge-Based Radar Detection, Tracking, and Classification, 75–101. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2007. http://dx.doi.org/10.1002/9780470283158.ch5.

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Conference papers on the topic "Tracking radar Data processing"

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Balaji, Bhashyam. "Bayesian radar data cube processing and syntactic tracking." In SPIE Defense, Security, and Sensing, edited by Ivan Kadar. SPIE, 2011. http://dx.doi.org/10.1117/12.883531.

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Liang, Huaiyuan, Pengcheng Wang, and Xiangrong Wang. "Simultaneous Tracking of Multiple Targets using Interferometric FMCW Radar." In 2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP). IEEE, 2019. http://dx.doi.org/10.1109/icsidp47821.2019.9173048.

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Zhengxiang, Lan, Zhang Yaotian, Yang Bin, Wang Jun, and Zhang Yuxi. "Vehicle Tracking in Clutter Environment using High Resolution Radar." In 2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP). IEEE, 2019. http://dx.doi.org/10.1109/icsidp47821.2019.9173128.

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Chervoniak, Yevhen, Rustem Sinitsyn, Felix Yanovsky, Vitalii Makarenko, Vadim Tokarev, and Oleksandr Zaporozhets. "Algorithm of Passive Acoustic Locator Data Processing for Flying Vehicle Detection and Tracking." In 2017 IEEE Microwaves, Radar and Remote Sensing Symposium (MRRS). IEEE, 2017. http://dx.doi.org/10.1109/mrrs.2017.8075021.

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Chan, Chun-Nien, and Carrson C. Fung. "Rfcm for Data Association and Multitarget Tracking Using 3D Radar." In ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. http://dx.doi.org/10.1109/icassp.2018.8461917.

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Kazimierski, Witold, and Andrzej Stateczny. "Fusion of data from AIS and tracking radar for the needs of ECDIS." In 2013 Signal Processing Symposium (SPS). IEEE, 2013. http://dx.doi.org/10.1109/sps.2013.6623592.

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Jianping Yao and Leonard Chin. "Variance estimation for a radar tracking system." In 1999 Information, Decision and Control. Data and Information Fusion Symposium, Signal Processing and Communications Symposium and Decision and Control Symposium. Proceedings (Cat. No.99EX251). IEEE, 1999. http://dx.doi.org/10.1109/idc.1999.754183.

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Zhuravlev, A., V. Razevig, M. Chizh, S. Ivashov, A. Bugaev, A. Kokoshkin, and V. Korotkov. "Data acquisition, processing, and visualization in microwave holography with probe tracking and positioning on video." In 2016 16th International Conference on Ground Penetrating Radar (GPR). IEEE, 2016. http://dx.doi.org/10.1109/icgpr.2016.7572628.

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Zhang, Yong-Peng, Bao-Ye Li, and Chao Gao. "Design of a Real-Time Monopulse Tracking Data Processing Algorithms for Missile-Borne SAR Radar." In 2019 6th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR). IEEE, 2019. http://dx.doi.org/10.1109/apsar46974.2019.9048327.

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Rajeswari, K., A. Ishwarya, K. K. Vaishnavi, and S. J. Thiruvengadam. "Performance analysis of data fusion methods for radar and IRST 3D target tracking." In 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET). IEEE, 2017. http://dx.doi.org/10.1109/wispnet.2017.8300227.

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Reports on the topic "Tracking radar Data processing"

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Stirman, Charles. Applications of Wavelets to Radar Data Processing. Fort Belvoir, VA: Defense Technical Information Center, July 1991. http://dx.doi.org/10.21236/ada239297.

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Cottrill, Stanley D. Tracking Radar Advanced Signal Processing and Computing for Kwajalein Atoll (KA) Application. Fort Belvoir, VA: Defense Technical Information Center, November 1992. http://dx.doi.org/10.21236/ada259066.

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Chouikha, Mohamed F. A Study of Inverse Methods for Processing of Radar Data. Fort Belvoir, VA: Defense Technical Information Center, October 2006. http://dx.doi.org/10.21236/ada462060.

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Ly, Canh. Radar Array Processing of Experimental Data Via the Scan-MUSIC Algorithm. Fort Belvoir, VA: Defense Technical Information Center, June 2004. http://dx.doi.org/10.21236/ada425842.

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Lebak, J. M., J. S. McMahon, and M. Arakawa. Polymorphous Computing Architecture (PCA) Application Benchmark 1: Three-Dimensional Radar Data Processing. Fort Belvoir, VA: Defense Technical Information Center, November 2001. http://dx.doi.org/10.21236/ada422824.

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Powers, Michael H. Improving Ground Penetrating Radar Imaging in High Loss Environments by Coordinated System Development, Data Processing, Numerical Modeling, & Visualization ... Office of Scientific and Technical Information (OSTI), June 2003. http://dx.doi.org/10.2172/838446.

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Wright, David L. Improving Ground Penetrating Radar Imaging in High Loss Environments by Coordinated System Development, Data Processing, Numerical Modeling, & Visualization. Office of Scientific and Technical Information (OSTI), December 2004. http://dx.doi.org/10.2172/850393.

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Shah, Rajiv R. High-Level Adaptive Signal Processing Architecture with Applications to Radar Non-Gaussian Clutter. Volume 2. A New Technique for Distribution Approximation of Random Data. Fort Belvoir, VA: Defense Technical Information Center, September 1995. http://dx.doi.org/10.21236/ada300902.

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David Wright, Michael Powers, Charles Oden, and Craig Moulton. Improving Ground Penetrating Radar Imaging in High Loss Environments by Coordinated System Development, Data Processing, Numerical Modeling, and Visualization methods with Applications to Site Characterization. Office of Scientific and Technical Information (OSTI), October 2006. http://dx.doi.org/10.2172/895009.

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Wright, David L. Improving Ground Penetrating Radar Imaging in High Loss Environments by Coordinated System Development, Data Processing, Numerical Modeling, and Visualization Methods with Applications to Site Characterization. Office of Scientific and Technical Information (OSTI), June 2003. http://dx.doi.org/10.2172/838443.

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