Pub Date : 2010-05-10DOI: 10.1109/RADAR.2010.5494396
J. Baker, J. Ruohoniemi, A. J. Ribeiro, L. Clausen, R. Greenwald, N. Frissell, K. A. Sterne
The Super Dual Auroral Radar Network (SuperDARN) of high frequency radars monitors ionospheric space weather at middle to high latitudes in both hemispheres. SuperDARN is an international collaboration involving scientists and engineers from over a dozen countries. The backscatter targets of interest are irregularities in the ionospheric plasma density that are aligned along the geomagnetic field. The Doppler motion of the irregularities can be used to infer the strength and direction of the ionospheric electric field. These measurements, obtained continuously, provide valuable information about the electrodynamics of the coupled magnetosphere-ionosphere system over extended spatial scales and with high time resolution. In this paper, the history of SuperDARN is briefly reviewed with a particular emphasis on the recent expansion of the network to middle and higher latitudes. A technique for assimilating multi-radar data to produce space weather maps of the hemispheric state of ionospheric plasma motion is also described.
{"title":"Monitoring ionospheric space weather with the Super Dual Auroral Radar Network (SuperDARN)","authors":"J. Baker, J. Ruohoniemi, A. J. Ribeiro, L. Clausen, R. Greenwald, N. Frissell, K. A. Sterne","doi":"10.1109/RADAR.2010.5494396","DOIUrl":"https://doi.org/10.1109/RADAR.2010.5494396","url":null,"abstract":"The Super Dual Auroral Radar Network (SuperDARN) of high frequency radars monitors ionospheric space weather at middle to high latitudes in both hemispheres. SuperDARN is an international collaboration involving scientists and engineers from over a dozen countries. The backscatter targets of interest are irregularities in the ionospheric plasma density that are aligned along the geomagnetic field. The Doppler motion of the irregularities can be used to infer the strength and direction of the ionospheric electric field. These measurements, obtained continuously, provide valuable information about the electrodynamics of the coupled magnetosphere-ionosphere system over extended spatial scales and with high time resolution. In this paper, the history of SuperDARN is briefly reviewed with a particular emphasis on the recent expansion of the network to middle and higher latitudes. A technique for assimilating multi-radar data to produce space weather maps of the hemispheric state of ionospheric plasma motion is also described.","PeriodicalId":125591,"journal":{"name":"2010 IEEE Radar Conference","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134312481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-05-10DOI: 10.1109/RADAR.2010.5494519
B. Mobasseri, G. Smith, Imad Estephan
Sensing through the wall using radar is a valuable capability. There is considerable work in generating radar images of the interior of a room by beamforming of the radar backscatter generated in a Synthetic Aperture Radar (SAR) configuration. However, high level interpretation of the scene is a more difficult task. In previous work a minimum distance classifier was successfully used to recognize various targets placed in the scene. The approach suffered from the dependency of target features on target location. This work presents a solution to this problem by bringing the target intensity profiles into alignment with the training data prior to classification. The alignment is performed by moving the intensity profiles between locations using an Autoregressive Moving Average (ARMA) model. The classification results before and after alignment show a marked improvement.
{"title":"A target alignment algorithm for through-the-wall radar imagery classification","authors":"B. Mobasseri, G. Smith, Imad Estephan","doi":"10.1109/RADAR.2010.5494519","DOIUrl":"https://doi.org/10.1109/RADAR.2010.5494519","url":null,"abstract":"Sensing through the wall using radar is a valuable capability. There is considerable work in generating radar images of the interior of a room by beamforming of the radar backscatter generated in a Synthetic Aperture Radar (SAR) configuration. However, high level interpretation of the scene is a more difficult task. In previous work a minimum distance classifier was successfully used to recognize various targets placed in the scene. The approach suffered from the dependency of target features on target location. This work presents a solution to this problem by bringing the target intensity profiles into alignment with the training data prior to classification. The alignment is performed by moving the intensity profiles between locations using an Autoregressive Moving Average (ARMA) model. The classification results before and after alignment show a marked improvement.","PeriodicalId":125591,"journal":{"name":"2010 IEEE Radar Conference","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134516215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-05-10DOI: 10.1109/RADAR.2010.5494387
R. I. Barnes, G. Earl, M. Papazoglou, L. Burchett, A. Terzuoli
Modern OTHR systems make extensive use of propagation support information for parameter setup advice. A novel method for increasing dimensionality and temporal resolution of this advice is demonstrated here using an instantaneously wideband waveform on a one-way path. A composite of pseudo-randomly phased, discretized, massively multi-channel signals is synthesized through a simple summing scheme. Upon reception the composite is rapidly processed in the frequency domain to produce channel scattering information simultaneously across the band. The channels may be collapsed in the Doppler domain to reduce to conventional oblique ionograms. Total integration time required to produce the full Doppler ionograms, even with low transmit powers, is reduced over conventional methods by up to three orders of magnitude leading to the term ‘Instagram’. The technique is implemented on an oblique sounding system that provides the necessary direct digital arbitrary waveform generation and reception capability. A result from the initial field trial is provided.
{"title":"The instagram: A novel sounding technique for enhanced HF propagation advice","authors":"R. I. Barnes, G. Earl, M. Papazoglou, L. Burchett, A. Terzuoli","doi":"10.1109/RADAR.2010.5494387","DOIUrl":"https://doi.org/10.1109/RADAR.2010.5494387","url":null,"abstract":"Modern OTHR systems make extensive use of propagation support information for parameter setup advice. A novel method for increasing dimensionality and temporal resolution of this advice is demonstrated here using an instantaneously wideband waveform on a one-way path. A composite of pseudo-randomly phased, discretized, massively multi-channel signals is synthesized through a simple summing scheme. Upon reception the composite is rapidly processed in the frequency domain to produce channel scattering information simultaneously across the band. The channels may be collapsed in the Doppler domain to reduce to conventional oblique ionograms. Total integration time required to produce the full Doppler ionograms, even with low transmit powers, is reduced over conventional methods by up to three orders of magnitude leading to the term ‘Instagram’. The technique is implemented on an oblique sounding system that provides the necessary direct digital arbitrary waveform generation and reception capability. A result from the initial field trial is provided.","PeriodicalId":125591,"journal":{"name":"2010 IEEE Radar Conference","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132021326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-05-10DOI: 10.1109/RADAR.2010.5494580
Andres Rodriguez, Jeffrey Panza, B. Kumar, Abhijit Mahalanobis
Automatic target recognition (ATR) systems require detection, recognition, and tracking algorithms. The classical approach is to treat these three stages separately. In this paper, we investigate a correlation filter (CF)-based approach that combines these tasks for enhanced ATR. We present a Kalman filter framework to combine information from successive correlation outputs in a probabilistic way. Our contribution is a framework that is able to locate multiple targets with different velocities at unknown positions providing enhanced ATR with only a marginal increase in computation over other CF ATR algorithms.
{"title":"Automatic recognition of multiple targets with varying velocities using quadratic correlation filters and Kalman filters","authors":"Andres Rodriguez, Jeffrey Panza, B. Kumar, Abhijit Mahalanobis","doi":"10.1109/RADAR.2010.5494580","DOIUrl":"https://doi.org/10.1109/RADAR.2010.5494580","url":null,"abstract":"Automatic target recognition (ATR) systems require detection, recognition, and tracking algorithms. The classical approach is to treat these three stages separately. In this paper, we investigate a correlation filter (CF)-based approach that combines these tasks for enhanced ATR. We present a Kalman filter framework to combine information from successive correlation outputs in a probabilistic way. Our contribution is a framework that is able to locate multiple targets with different velocities at unknown positions providing enhanced ATR with only a marginal increase in computation over other CF ATR algorithms.","PeriodicalId":125591,"journal":{"name":"2010 IEEE Radar Conference","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132228299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-05-10DOI: 10.1109/RADAR.2010.5494433
Sang Hoon Baek, Hyunchul Seok, Kyu Ho Park, J. Chun
In multi-functional radar, task scheduling algorithm should be designed such that timing resource is efficiently utilized by functions such as surveillance and tracking, and its performance is maximized. In the target tracking, the tasks are required to be executed to consider the maneuvering motion, measurement condition and required tracking performance. Frequent execution of tracking tasks results in not only precise tracking, but also waste of timing resource which is shared with other functions. Therefore, to reduce the number of unnecessary observations, the tracking task is required to be executed only when the update is needed. In this paper, the innovation, position residual, in Kalman filter is used as reference value for adjusting update rate of tracking tasks. Using feedback controller, the update rate is allocated so that predicted observation is expected to be within specified error range. In addition, targets are classified into 7 priorities according to tactical characteristics, and target's priority is also used as reference value for calculating update rate. The simulation results show that the proposed method reduces the tracking error of the target on maneuvering movement compared to fixed update rate case.
{"title":"An adaptive update-rate control of a phased array radar for efficient usage of tracking tasks","authors":"Sang Hoon Baek, Hyunchul Seok, Kyu Ho Park, J. Chun","doi":"10.1109/RADAR.2010.5494433","DOIUrl":"https://doi.org/10.1109/RADAR.2010.5494433","url":null,"abstract":"In multi-functional radar, task scheduling algorithm should be designed such that timing resource is efficiently utilized by functions such as surveillance and tracking, and its performance is maximized. In the target tracking, the tasks are required to be executed to consider the maneuvering motion, measurement condition and required tracking performance. Frequent execution of tracking tasks results in not only precise tracking, but also waste of timing resource which is shared with other functions. Therefore, to reduce the number of unnecessary observations, the tracking task is required to be executed only when the update is needed. In this paper, the innovation, position residual, in Kalman filter is used as reference value for adjusting update rate of tracking tasks. Using feedback controller, the update rate is allocated so that predicted observation is expected to be within specified error range. In addition, targets are classified into 7 priorities according to tactical characteristics, and target's priority is also used as reference value for calculating update rate. The simulation results show that the proposed method reduces the tracking error of the target on maneuvering movement compared to fixed update rate case.","PeriodicalId":125591,"journal":{"name":"2010 IEEE Radar Conference","volume":"355 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132793374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-05-10DOI: 10.1109/RADAR.2010.5494464
A. Fam
Good code sets have autocorrelation functions ACF with small sidelobes, and also have small crosscorrelations. In this work, a class of good ternary codes sets are introduced. First, mutually orthogonal vectors are selected, then they are spread via a Golomb ruler. This is shown to result in such a good set. If the mutually orthogonal vectors have entries in {-1,1} or {-1,0,1}, then a ternary code set result. While there are methods of generating ternary codes, and complementary ternary codes [1–7], there is no method in prior publications of generating mutually orthogonal ternary code sets. That is one of the contributions of this work. If complex numbers with unity magnitudes are allowed, then we obtain codes with magnitudes in {0,1}. If the vectors are obtained from matrices with mutually orthogonal rows and columns, as in Hadamard matrices, or DFT matrices, then longer codes can be obtained via spreading the obtained good set via a Golomb ruler a second time. Using existing codes, such as Barker codes, and spreading them via a Golomb ruler, then compounding them with the elements of a good set, results in a new good set with higher mainlobes. The spreading could be induced via any array of any dimension with elements of magnitudes in {0,1} that have autocorrelation with unity peak sidelobes. This includes Costas arrays, in addition to Golomb rulers.
{"title":"Good code sets by spreading orthogonal vectors via Golomb rulers and Costas arrays","authors":"A. Fam","doi":"10.1109/RADAR.2010.5494464","DOIUrl":"https://doi.org/10.1109/RADAR.2010.5494464","url":null,"abstract":"Good code sets have autocorrelation functions ACF with small sidelobes, and also have small crosscorrelations. In this work, a class of good ternary codes sets are introduced. First, mutually orthogonal vectors are selected, then they are spread via a Golomb ruler. This is shown to result in such a good set. If the mutually orthogonal vectors have entries in {-1,1} or {-1,0,1}, then a ternary code set result. While there are methods of generating ternary codes, and complementary ternary codes [1–7], there is no method in prior publications of generating mutually orthogonal ternary code sets. That is one of the contributions of this work. If complex numbers with unity magnitudes are allowed, then we obtain codes with magnitudes in {0,1}. If the vectors are obtained from matrices with mutually orthogonal rows and columns, as in Hadamard matrices, or DFT matrices, then longer codes can be obtained via spreading the obtained good set via a Golomb ruler a second time. Using existing codes, such as Barker codes, and spreading them via a Golomb ruler, then compounding them with the elements of a good set, results in a new good set with higher mainlobes. The spreading could be induced via any array of any dimension with elements of magnitudes in {0,1} that have autocorrelation with unity peak sidelobes. This includes Costas arrays, in addition to Golomb rulers.","PeriodicalId":125591,"journal":{"name":"2010 IEEE Radar Conference","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122287692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-05-10DOI: 10.1109/RADAR.2010.5494578
Sajid Ahmed, J. Thompson, B. Mulgrew, Y. Pétillot
Designing transmit beampattern with MIMO radars generally requires the waveforms to be able to have arbitrary cross-correlation values. In contrast to the available algorithms, the proposed technique provides a closed-form solution for the synthesis of covariance matrix, R, of the waveforms to obtain desired beampattern match. To synthesis R the constraints and redundant information in R are leveraged, which convert the constrained problem into un-constrained problem. Next a novel method for generating the constant-envelope (CE) waveforms to realise the synthesised covariance matrix, R, is proposed. This method also yields a closed-form solution and choose the symbols from the binary-phase shift-keying (BPSK). Here, Gaussian random-variables (RV's) are mapped onto the CE RV's by a memoryless non-linear transformation, which converts the problem of finding the non-Gaussian RV's to realise a given covariance matrix R into finding the Gaussian RV's to realise covariance matrix Rg. Simulation results are presented to demonstrate the effectiveness of both methodologies.
{"title":"Fast computations of constant envelope waveforms for MIMO radar transmit beampattern","authors":"Sajid Ahmed, J. Thompson, B. Mulgrew, Y. Pétillot","doi":"10.1109/RADAR.2010.5494578","DOIUrl":"https://doi.org/10.1109/RADAR.2010.5494578","url":null,"abstract":"Designing transmit beampattern with MIMO radars generally requires the waveforms to be able to have arbitrary cross-correlation values. In contrast to the available algorithms, the proposed technique provides a closed-form solution for the synthesis of covariance matrix, R, of the waveforms to obtain desired beampattern match. To synthesis R the constraints and redundant information in R are leveraged, which convert the constrained problem into un-constrained problem. Next a novel method for generating the constant-envelope (CE) waveforms to realise the synthesised covariance matrix, R, is proposed. This method also yields a closed-form solution and choose the symbols from the binary-phase shift-keying (BPSK). Here, Gaussian random-variables (RV's) are mapped onto the CE RV's by a memoryless non-linear transformation, which converts the problem of finding the non-Gaussian RV's to realise a given covariance matrix R into finding the Gaussian RV's to realise covariance matrix Rg. Simulation results are presented to demonstrate the effectiveness of both methodologies.","PeriodicalId":125591,"journal":{"name":"2010 IEEE Radar Conference","volume":"8 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114006886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-05-10DOI: 10.1109/RADAR.2010.5494538
R. Raj, V. Chen, R. Lipps
We present a novel algorithm for radar imaging of point scatterers using a sparse number of spatially separated sensors. Such sparse sensing scenarios are prototypical of many applications wherein a limited number of sensors are distributed over a geographical area; or where environmental and/or systemic constraints enforce a sparse sampling of angular aperture. Our underlying assumption is that the image is sparse with respect to the Gabor basis set. We then introduce the concept of an orbit-viz. the locus of all projections made by a spatial basis-and formulate the radar imaging problem as that of sparsifying the number of orbits that comprise the radon measurements of the source. We demonstrate how our algorithm outperforms FFT-based and Compressive-sensing based reconstruction algorithms for point-scatterer images, describe relevant theoretical performance bounds of our algorithm, and point to future research arising from this work.
{"title":"A greedy approach for sparse angular aperture radar","authors":"R. Raj, V. Chen, R. Lipps","doi":"10.1109/RADAR.2010.5494538","DOIUrl":"https://doi.org/10.1109/RADAR.2010.5494538","url":null,"abstract":"We present a novel algorithm for radar imaging of point scatterers using a sparse number of spatially separated sensors. Such sparse sensing scenarios are prototypical of many applications wherein a limited number of sensors are distributed over a geographical area; or where environmental and/or systemic constraints enforce a sparse sampling of angular aperture. Our underlying assumption is that the image is sparse with respect to the Gabor basis set. We then introduce the concept of an orbit-viz. the locus of all projections made by a spatial basis-and formulate the radar imaging problem as that of sparsifying the number of orbits that comprise the radon measurements of the source. We demonstrate how our algorithm outperforms FFT-based and Compressive-sensing based reconstruction algorithms for point-scatterer images, describe relevant theoretical performance bounds of our algorithm, and point to future research arising from this work.","PeriodicalId":125591,"journal":{"name":"2010 IEEE Radar Conference","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116006865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-05-10DOI: 10.1109/RADAR.2010.5494431
Mei Li, R. Evans, E. Skafidas, B. Moran
We discuss a single chip millimeter wave direct-conversion transceiver fabricated using 65 nm Bulk CMOS technology to produce a low power, ultra low cost radar-on-a-chip (ROACH). The proposed ROACH system operates at carrier frequencies around 77 GHz, and is capable of detecting a 0.5 square meter target at several hundred meters. We present a novel technology based radar equation, called the ROACH Equation, which presents radar performance in terms of technology parameters. In addition, building on the precise relationship between differential phase noise and coherent integration duration, we establish optimal conditions for switching between coherent integration mode and incoherent integration modes. Numerical examples demonstrate that the proposed integration scheme effectively extends the maximum detection range of the single chip radar with associated benefit of reduced computational cost and hardware implementation complexity.
{"title":"Radar-on-a-chip (ROACH)","authors":"Mei Li, R. Evans, E. Skafidas, B. Moran","doi":"10.1109/RADAR.2010.5494431","DOIUrl":"https://doi.org/10.1109/RADAR.2010.5494431","url":null,"abstract":"We discuss a single chip millimeter wave direct-conversion transceiver fabricated using 65 nm Bulk CMOS technology to produce a low power, ultra low cost radar-on-a-chip (ROACH). The proposed ROACH system operates at carrier frequencies around 77 GHz, and is capable of detecting a 0.5 square meter target at several hundred meters. We present a novel technology based radar equation, called the ROACH Equation, which presents radar performance in terms of technology parameters. In addition, building on the precise relationship between differential phase noise and coherent integration duration, we establish optimal conditions for switching between coherent integration mode and incoherent integration modes. Numerical examples demonstrate that the proposed integration scheme effectively extends the maximum detection range of the single chip radar with associated benefit of reduced computational cost and hardware implementation complexity.","PeriodicalId":125591,"journal":{"name":"2010 IEEE Radar Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115977541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-05-10DOI: 10.1109/RADAR.2010.5494574
M. Pace
The Probability Hypothesis Density (PHD) filter is applied to realistic three-dimensional aerial and naval scenarios to illustrate its performance in detecting, initiating and terminating tracks in presence of clutter. Radar measurements are available every two seconds. A comparisons between different approximations of the PHD recursion, namely the sequential Monte Carlo and the Gaussian Mixture approximation, is given on different scenarios using the OSPA metric and different levels of clutter.
{"title":"Comparison of PHD based filters for the tracking of 3D aerial and naval scenarios","authors":"M. Pace","doi":"10.1109/RADAR.2010.5494574","DOIUrl":"https://doi.org/10.1109/RADAR.2010.5494574","url":null,"abstract":"The Probability Hypothesis Density (PHD) filter is applied to realistic three-dimensional aerial and naval scenarios to illustrate its performance in detecting, initiating and terminating tracks in presence of clutter. Radar measurements are available every two seconds. A comparisons between different approximations of the PHD recursion, namely the sequential Monte Carlo and the Gaussian Mixture approximation, is given on different scenarios using the OSPA metric and different levels of clutter.","PeriodicalId":125591,"journal":{"name":"2010 IEEE Radar Conference","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114825033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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