Pub Date : 2022-07-04DOI: 10.23919/fusion49751.2022.9841237
Martin Michaelis, Philipp Berthold, T. Luettel, Hans-Joachim Wünsche
This paper presents an extended object tracking algorithm. We model visibility constraints in the measurement process. A sequential importance sampling and resampling particle filter is used, which permits a flexible modeling of the target shape. We compare our method to the standard approach of just rotating and translating the target extent model. Our approach is applicable for both radar and LiDAR sensors. Results are presented in a a simulated scenario with LiDAR data. A proof of concept is conducted in a real world scenario with radar data.
{"title":"Extended Target Tracking with a Particle Filter Using State Dependent Target Measurement Models","authors":"Martin Michaelis, Philipp Berthold, T. Luettel, Hans-Joachim Wünsche","doi":"10.23919/fusion49751.2022.9841237","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841237","url":null,"abstract":"This paper presents an extended object tracking algorithm. We model visibility constraints in the measurement process. A sequential importance sampling and resampling particle filter is used, which permits a flexible modeling of the target shape. We compare our method to the standard approach of just rotating and translating the target extent model. Our approach is applicable for both radar and LiDAR sensors. Results are presented in a a simulated scenario with LiDAR data. A proof of concept is conducted in a real world scenario with radar data.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122790935","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 : 2022-07-04DOI: 10.23919/fusion49751.2022.9841253
M. Bochkati, Jürgen Dampf, T. Pany
Within an ultra-tightly (or deeply) integrated global navigation satellite system (GNSS) and inertial navigation system (INS) GNSS/INS, GNSS signal correlation delivers correlator values as input to the integration filter. On the other side, the integration filter controls the correlation process by determining the numerically controlled oscillator (NCO) values. As GNSS signal correlation is a computational trivial but a time-consuming process, we propose for R&D in this area an alternative approach to first generate for each GNSS signal multi-correlator values and store them for the later GNSS/INS filter development work. Once the filter runs, it interpolates from the multi-correlator values the actual needed correlation values. The multi-correlator values thus act like a data compression for the GNSS signals. This paper discusses the mathematical framework for this data compression, which is loosely described as a sufficient statistic. This statistic consists of the correlation values themselves plus the NCO values that have been used during the correlation process. The generation and interpolation process will be described in this contribution with all mathematical details, as well as interpolation limits in code phase and Doppler direction. Finally, this approach is validated by comparison of global positioning system (GPS) C/A code pseudorange and carrier phase data from direct tracking to results originate from a MATLAB-based receiver which uses the multi-correlator values as sufficient statistics.
{"title":"On the Use of Multi-correlator Values as Sufficient Statistics as Basis for Flexible Ultra-tight GNSS/INS Integration Developments","authors":"M. Bochkati, Jürgen Dampf, T. Pany","doi":"10.23919/fusion49751.2022.9841253","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841253","url":null,"abstract":"Within an ultra-tightly (or deeply) integrated global navigation satellite system (GNSS) and inertial navigation system (INS) GNSS/INS, GNSS signal correlation delivers correlator values as input to the integration filter. On the other side, the integration filter controls the correlation process by determining the numerically controlled oscillator (NCO) values. As GNSS signal correlation is a computational trivial but a time-consuming process, we propose for R&D in this area an alternative approach to first generate for each GNSS signal multi-correlator values and store them for the later GNSS/INS filter development work. Once the filter runs, it interpolates from the multi-correlator values the actual needed correlation values. The multi-correlator values thus act like a data compression for the GNSS signals. This paper discusses the mathematical framework for this data compression, which is loosely described as a sufficient statistic. This statistic consists of the correlation values themselves plus the NCO values that have been used during the correlation process. The generation and interpolation process will be described in this contribution with all mathematical details, as well as interpolation limits in code phase and Doppler direction. Finally, this approach is validated by comparison of global positioning system (GPS) C/A code pseudorange and carrier phase data from direct tracking to results originate from a MATLAB-based receiver which uses the multi-correlator values as sufficient statistics.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121988207","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 : 2022-07-04DOI: 10.48550/arXiv.2207.06156
'Angel F. Garc'ia-Fern'andez, Yuxuan Xia, L. Svensson
This paper provides a comparative analysis between the adaptive birth model used in the labelled random finite set literature and the track initiation in the Poisson multi-Bernoulli mixture (PMBM) filter, with point-target models. The PMBM track initiation is obtained via Bayes' rule applied on the pre-dicted PMBM density, and creates one Bernoulli component for each received measurement, representing that this measurement may be clutter or a detection from a new target. Adaptive birth mimics this procedure by creating a Bernoulli component for each measurement using a different rule to determine the probability of existence and a user-defined single-target density. This paper first provides an analysis of the differences that arise in track initiation based on isolated measurements. Then, it shows that adaptive birth underestimates the number of objects present in the surveillance area under common modelling assumptions. Finally, we provide numerical simulations to further illustrate the differences.
{"title":"A comparison between PMBM Bayesian track initiation and labelled RFS adaptive birth","authors":"'Angel F. Garc'ia-Fern'andez, Yuxuan Xia, L. Svensson","doi":"10.48550/arXiv.2207.06156","DOIUrl":"https://doi.org/10.48550/arXiv.2207.06156","url":null,"abstract":"This paper provides a comparative analysis between the adaptive birth model used in the labelled random finite set literature and the track initiation in the Poisson multi-Bernoulli mixture (PMBM) filter, with point-target models. The PMBM track initiation is obtained via Bayes' rule applied on the pre-dicted PMBM density, and creates one Bernoulli component for each received measurement, representing that this measurement may be clutter or a detection from a new target. Adaptive birth mimics this procedure by creating a Bernoulli component for each measurement using a different rule to determine the probability of existence and a user-defined single-target density. This paper first provides an analysis of the differences that arise in track initiation based on isolated measurements. Then, it shows that adaptive birth underestimates the number of objects present in the surveillance area under common modelling assumptions. Finally, we provide numerical simulations to further illustrate the differences.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129741965","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 : 2022-07-04DOI: 10.23919/fusion49751.2022.9841285
Xianqing Li, Z. Duan, U. Hanebeck
The performance evaluation for joint state and parameter estimation (JSPE) is of great significance. Joint Cramér-Rao lower bound (JCRLB) has been widely studied for JSPE of nonlinear parametric systems with white noise. However, in practice, the noise is often colored due to high measurement frequency and bandlimited signal channels. In this paper, a recursive JCRLB is developed for JSPE of nonlinear parametric systems with colored noise, characterized by auto-regressive (AR) models. First, we propose a unified recursive JCRLB for JSPE of general nonlinear parametric systems with higher-order autocorrelated process noises and autocorrelated measurement noise simultaneously. Then its relationship with the posterior Cramér-Rao lower bound (PCRLB) for filtering of nonlinear systems with colored noise and the hybrid Cramér-Rao lower bound (HCRLB) for JSPE of regular parametric systems with white noise are provided. Illustrative examples in radar target tracking verify the effectiveness of the proposed JCRLB for the performance evaluation for JSPE of nonlinear parametric systems with colored noise.
{"title":"Recursive Joint Cramér-Rao Lower Bound for Nonlinear Parametric Systems with Colored Noise","authors":"Xianqing Li, Z. Duan, U. Hanebeck","doi":"10.23919/fusion49751.2022.9841285","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841285","url":null,"abstract":"The performance evaluation for joint state and parameter estimation (JSPE) is of great significance. Joint Cramér-Rao lower bound (JCRLB) has been widely studied for JSPE of nonlinear parametric systems with white noise. However, in practice, the noise is often colored due to high measurement frequency and bandlimited signal channels. In this paper, a recursive JCRLB is developed for JSPE of nonlinear parametric systems with colored noise, characterized by auto-regressive (AR) models. First, we propose a unified recursive JCRLB for JSPE of general nonlinear parametric systems with higher-order autocorrelated process noises and autocorrelated measurement noise simultaneously. Then its relationship with the posterior Cramér-Rao lower bound (PCRLB) for filtering of nonlinear systems with colored noise and the hybrid Cramér-Rao lower bound (HCRLB) for JSPE of regular parametric systems with white noise are provided. Illustrative examples in radar target tracking verify the effectiveness of the proposed JCRLB for the performance evaluation for JSPE of nonlinear parametric systems with colored noise.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128782978","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 : 2022-07-04DOI: 10.23919/fusion49751.2022.9841228
Federico Urli, Emiliano Versini, L. Snidaro
The availability of a vast quantity of information from news channels and social media, make it often difficult to find and follow specific events. This applies to both casual readers and to intelligence and emergency response analysts. In particular, the latter need to find and process relevant information within sense-making, situation and impact assessment processes. The automatic retrieval and tracking of news has been addressed by a good number of works in the information retrieval literature. However, there is a strong potential for introducing automatic systems employing information fusion methods and techniques to assist decision makers. In the field of deep learning, several techniques for text encoding have been proposed, which have allowed significant progress also in the field of news retrieval and ranking. The objective of this paper is to explore the usage and combination of different pre-trained sentence embeddings, including multimodal ones, obtained from different parts of text that compose a news story. This in order to understand which type of technique is best for encoding the different information available in online news.
{"title":"Fusion of sentence embeddings for news retrieval","authors":"Federico Urli, Emiliano Versini, L. Snidaro","doi":"10.23919/fusion49751.2022.9841228","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841228","url":null,"abstract":"The availability of a vast quantity of information from news channels and social media, make it often difficult to find and follow specific events. This applies to both casual readers and to intelligence and emergency response analysts. In particular, the latter need to find and process relevant information within sense-making, situation and impact assessment processes. The automatic retrieval and tracking of news has been addressed by a good number of works in the information retrieval literature. However, there is a strong potential for introducing automatic systems employing information fusion methods and techniques to assist decision makers. In the field of deep learning, several techniques for text encoding have been proposed, which have allowed significant progress also in the field of news retrieval and ranking. The objective of this paper is to explore the usage and combination of different pre-trained sentence embeddings, including multimodal ones, obtained from different parts of text that compose a news story. This in order to understand which type of technique is best for encoding the different information available in online news.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"154 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129167624","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 : 2022-07-04DOI: 10.23919/fusion49751.2022.9841293
Joshua Hiatt, Clark N. Taylor
Navigation using a Global Navigation Satellite System (GNSS) is common for autonomous vehicles (ground or air). Unfortunately, GNSS-based navigation solutions are often susceptible to jamming, interference, and a limited number of satellites. A proposed technique to aid in navigation when a GNSS-based system fails is magnetic navigation - navigation using the Earth's magnetic anomaly field. This solution comes with its own set of problems including the need for quality magnetic maps in every area in which magnetic navigation will be used. Many of the currently available magnetic maps are generated from a combination of dated magnetic surveys, resulting in maps riddled with spatially correlated errors, the correlation structure of which is largely unknown. The correlations are further confounded while navigating because they depend on how fast a vehicle moves through the map in addition to the original correlated error structure. Traditionally, this spatial correlation has been handled by introducing a First Order Gauss-Markov (FOGM) noise model into the estimation routine, with the FOGM parameters set somewhat arbitrarily. In this paper, we investigate the possibility of using correlation agnostic fusion techniques (i.e., Covariance Intersection and Probabilistically Conservative Fusion) for magnetic navigation. These techniques have the advantage of not requiring any parameter tuning; the same method and tuning parameters are used regardless of the spatial correlation. We demonstrate that utilizing probabilistically conservative fusion leads to navigation results that are better than many tuned approaches and reasonably close to the best possible tuning parameters of a FOGM.
{"title":"A Comparison of Correlation-Agnostic Techniques for Magnetic Navigation","authors":"Joshua Hiatt, Clark N. Taylor","doi":"10.23919/fusion49751.2022.9841293","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841293","url":null,"abstract":"Navigation using a Global Navigation Satellite System (GNSS) is common for autonomous vehicles (ground or air). Unfortunately, GNSS-based navigation solutions are often susceptible to jamming, interference, and a limited number of satellites. A proposed technique to aid in navigation when a GNSS-based system fails is magnetic navigation - navigation using the Earth's magnetic anomaly field. This solution comes with its own set of problems including the need for quality magnetic maps in every area in which magnetic navigation will be used. Many of the currently available magnetic maps are generated from a combination of dated magnetic surveys, resulting in maps riddled with spatially correlated errors, the correlation structure of which is largely unknown. The correlations are further confounded while navigating because they depend on how fast a vehicle moves through the map in addition to the original correlated error structure. Traditionally, this spatial correlation has been handled by introducing a First Order Gauss-Markov (FOGM) noise model into the estimation routine, with the FOGM parameters set somewhat arbitrarily. In this paper, we investigate the possibility of using correlation agnostic fusion techniques (i.e., Covariance Intersection and Probabilistically Conservative Fusion) for magnetic navigation. These techniques have the advantage of not requiring any parameter tuning; the same method and tuning parameters are used regardless of the spatial correlation. We demonstrate that utilizing probabilistically conservative fusion leads to navigation results that are better than many tuned approaches and reasonably close to the best possible tuning parameters of a FOGM.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"54 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113957835","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 : 2022-07-04DOI: 10.23919/fusion49751.2022.9841310
Kolja Thormann, M. Baum
Track-to-track fusion considers the problem of fusing multiple tracks (e.g., from different sensor nodes) of the same target object. In case the spatial extent of the target object is estimated, unique challenges for the track-to-track fusion method arise, e.g., there may be ambiguities in the parameterization. In this work, we present an approach for distributed track-to-track fusion in case of elliptical extend targets, where correlations from common prior and process noise are explicitly incorporated. The approach is based on the previously introduced Random Ellipse Density (RED) framework, which deals with ambiguous ellipse representations and utilizes a Minimum Mean Gaussian Wasserstein (MMGW) estimator that is optimal with respect to the Gaussian Wasserstein (GW) distance. We provide simulated experiments in which measurements from different sensors are tracked by elliptic extended object trackers, fusing the trackers' estimates to improve the estimation.
{"title":"Track-to- Track Fusion for Elliptical Extended Targets Parameterized with Orientation and Semi-Axes Lengths","authors":"Kolja Thormann, M. Baum","doi":"10.23919/fusion49751.2022.9841310","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841310","url":null,"abstract":"Track-to-track fusion considers the problem of fusing multiple tracks (e.g., from different sensor nodes) of the same target object. In case the spatial extent of the target object is estimated, unique challenges for the track-to-track fusion method arise, e.g., there may be ambiguities in the parameterization. In this work, we present an approach for distributed track-to-track fusion in case of elliptical extend targets, where correlations from common prior and process noise are explicitly incorporated. The approach is based on the previously introduced Random Ellipse Density (RED) framework, which deals with ambiguous ellipse representations and utilizes a Minimum Mean Gaussian Wasserstein (MMGW) estimator that is optimal with respect to the Gaussian Wasserstein (GW) distance. We provide simulated experiments in which measurements from different sensors are tracked by elliptic extended object trackers, fusing the trackers' estimates to improve the estimation.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125766286","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 : 2022-07-04DOI: 10.23919/fusion49751.2022.9841357
Shidrokh Goudarzi, Wenwu Wang, P. Xiao, L. Mihaylova, S. Godsill
Unmanned aerial vehicles (UAVs) are useful devices due to their great manoeuvrability for long-range outdoor target tracking. However, these tracking tasks can lead to sub-optimal performance due to high computation requirements and power constraints. To cope with these challenges, we design a UAV-based target tracking algorithm where computationally intensive tasks are offloaded to Edge Computing (EC) servers. We perform joint optimization by considering the trade-off between transmission energy consumption and execution time to determine optimal edge nodes for task processing and reliable tracking. The simulation results demonstrate the superiority of the proposed UAV-based target tracking on the predefined trajectory over several existing techniques.
{"title":"UAV-enabled Edge Computing for Optimal Task Distribution in Target Tracking","authors":"Shidrokh Goudarzi, Wenwu Wang, P. Xiao, L. Mihaylova, S. Godsill","doi":"10.23919/fusion49751.2022.9841357","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841357","url":null,"abstract":"Unmanned aerial vehicles (UAVs) are useful devices due to their great manoeuvrability for long-range outdoor target tracking. However, these tracking tasks can lead to sub-optimal performance due to high computation requirements and power constraints. To cope with these challenges, we design a UAV-based target tracking algorithm where computationally intensive tasks are offloaded to Edge Computing (EC) servers. We perform joint optimization by considering the trade-off between transmission energy consumption and execution time to determine optimal edge nodes for task processing and reliable tracking. The simulation results demonstrate the superiority of the proposed UAV-based target tracking on the predefined trajectory over several existing techniques.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122259293","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 : 2022-07-04DOI: 10.23919/fusion49751.2022.9841233
Bernardo Camajori Tedeschini, Mattia Brambilla, Luca Barbieri, M. Nicoli
In cooperative localization systems, the fusion of information from multiple sensing platforms is acknowledged to improve localization accuracy of sensed targets. However, the data association required to perform the inference is non-trivial to be solved. In this context, we propose a graph formulation of the data association problem among unlabelled information produced at different sensors in which we run a Message Passing Neural Network (MPNN). The proposed MPNN algorithm suits for centralized sensing architectures where all sensors are connected to a single processing unit. We validate the theoretic aspects with numerical simulations in a vehicular scenario with cooperative lidar sensing. We show the robustness of the model against several environmental complexities such as high number of cooperative vehicles and different noise intensities.
{"title":"Addressing data association by message passing over graph neural networks","authors":"Bernardo Camajori Tedeschini, Mattia Brambilla, Luca Barbieri, M. Nicoli","doi":"10.23919/fusion49751.2022.9841233","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841233","url":null,"abstract":"In cooperative localization systems, the fusion of information from multiple sensing platforms is acknowledged to improve localization accuracy of sensed targets. However, the data association required to perform the inference is non-trivial to be solved. In this context, we propose a graph formulation of the data association problem among unlabelled information produced at different sensors in which we run a Message Passing Neural Network (MPNN). The proposed MPNN algorithm suits for centralized sensing architectures where all sensors are connected to a single processing unit. We validate the theoretic aspects with numerical simulations in a vehicular scenario with cooperative lidar sensing. We show the robustness of the model against several environmental complexities such as high number of cooperative vehicles and different noise intensities.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134125647","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 : 2022-07-04DOI: 10.23919/fusion49751.2022.9841320
M. Raitoharju, R. Hostettler, S. Särkkä
This paper is concerned with discrete time Kalman-type filtering with state transition and measurement noises that may be non-additive or non-linearly transformed. More specifically, we extend the iterative estimation algorithm Posterior Linearization Filter (PLF) for estimation with this kind of noises. The approach solves the prediction and update step simultaneously, which allows to use the PLF iterations to improve the estimation in the non-linear state transition model. The proposed algorithm also produces single step fixed-lag smoothing estimates. We show in examples how the proposed approach can be used with non-Gaussian state transition noises and non-linearly transformed state transition noises.
{"title":"Posterior linearisation filter for non-linear state transformation noises","authors":"M. Raitoharju, R. Hostettler, S. Särkkä","doi":"10.23919/fusion49751.2022.9841320","DOIUrl":"https://doi.org/10.23919/fusion49751.2022.9841320","url":null,"abstract":"This paper is concerned with discrete time Kalman-type filtering with state transition and measurement noises that may be non-additive or non-linearly transformed. More specifically, we extend the iterative estimation algorithm Posterior Linearization Filter (PLF) for estimation with this kind of noises. The approach solves the prediction and update step simultaneously, which allows to use the PLF iterations to improve the estimation in the non-linear state transition model. The proposed algorithm also produces single step fixed-lag smoothing estimates. We show in examples how the proposed approach can be used with non-Gaussian state transition noises and non-linearly transformed state transition noises.","PeriodicalId":176447,"journal":{"name":"2022 25th International Conference on Information Fusion (FUSION)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132772646","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：