Aliakbar Gorji - Academia.edu (original) (raw)
Papers by Aliakbar Gorji
2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 2008
Page 1. Artificial Neural Networks for Stochastic Control of Nonliner State Space Systems Ali A.G... more Page 1. Artificial Neural Networks for Stochastic Control of Nonliner State Space Systems Ali A.Gorji, Student Member, IEEE, and Mohammad B.Menhaj, Member, IEEE Abstract In this paper, stochastic control of nonlinear state space models is discussed. ...
2012 15th International Conference on Information Fusion, 2012
This paper deals with the antenna allocation problem in Multiple-Input Multiple-Output (MIMO) rad... more This paper deals with the antenna allocation problem in Multiple-Input Multiple-Output (MIMO) radar systems with collocated antennas. After deriving the Cramer-Rao Lower Bound (CRLB) as the cost function, the optimal distribution of antennas is found by applying the relevant operators to the CRLB. A convex optimization algorithm is then proposed to find the optimum distribution of antennas that achieves the optimal CRLB. It is also shown that the optimization problem can be simplified to the well-known Semi-definite Programming (SDP) for a single target scenario. Using a number of simulations, it is shown that the localization algorithm also leads to superior results when the optimal antenna configuration is used.
14th International Conference on Information Fusion, 2011
Performance evaluation is one of the most important steps in any target tracking problem. The obj... more Performance evaluation is one of the most important steps in any target tracking problem. The objective of this paper is to present a brief review of different approaches available for the performance analysis of multiple target tracking algorithms. Metrics are first classified into sensor-dependent and tracker-dependent ones. Then, the tracker-dependent measures are discussed after classifying into two groups named algorithm-free and algorithm dependent measures. For the classification purposes, three different categories of algorithm-free metrics are described. Finally, to demonstrate the application of the metrics in evaluating the performance of different tracking algorithms, a challenging scenario is considered.
2018 52nd Asilomar Conference on Signals, Systems, and Computers, 2018
Tracking multiple moving targets with bearing-only measurement is a challenging task, due to the ... more Tracking multiple moving targets with bearing-only measurement is a challenging task, due to the inherent difficulties in determining the correct trajectory of the observer that will meet observability conditions. The work presented here formulates Observer Trajectory Planning (OTP) as a continuous control problem, and proposes reinforcement learning as a solution. The proposed architecture in this work constitutes a model-independent framework that allows for the estimation of the states of targets, and that allows multiple targets to be tracked in a realistic scenario, where the agent has no prior information about the initial locations and velocities of the targets.
This thesis concerns with the localization, tracking, and sensor management in the Multiple-Input... more This thesis concerns with the localization, tracking, and sensor management in the Multiple-Input Multiple-Output (MIMO) radar systems. The collocated and widely-separated MIMO radars are separately discussed and the signal models are derived for both structures. The first chapter of the thesis is dedicated to the tracking and localization in collocated MIMO radars. A novel signal model is first formulated and the localization algorithm is developed for the derived signal model to estimate the location of multiple targets falling in the same resolution cell. Furthermore, a novel tracking algorithm is proposed in which the maximum bound on the number of uniquely detectable targets in the same cell is relaxed. The performance of the tracking and localization algorithms is finally evaluated using the tracking Posterior Cramer-Rao Lower Bound (PCRLB). After showing the impact of the antennas position on the localization CRLB, a novel sensor management technique is developed for the coll...
Signal and Data Processing of Small Targets 2009
ABSTRACT
2008 5th International Conference on the European Electricity Market, 2008
... Mutual information (MI) is very effective in evaluating the relevance of each input from the ... more ... Mutual information (MI) is very effective in evaluating the relevance of each input from the aspect of information theory. Lags (load of the previous hours) are considered as potential inputs. ... In using FSD for STLF, the normalization is based on the previous day data. ...
2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 2008
... Nariman Mahdavi, Ali A.Gorji, Mohammad B. Menhaj, and Saeedeh Barghinia ... A completely simi... more ... Nariman Mahdavi, Ali A.Gorji, Mohammad B. Menhaj, and Saeedeh Barghinia ... A completely similar manner can be also considered to compute Amerge and Asplit by computing p∗ K where K∗ is the new set of basis functions after merging or splitting old ones. ...
7th IFAC International Symposium on Advanced Control of Chemical Processes (2009), 2009
This paper presents a new grey-box state space model structure for nonlinear systems together wit... more This paper presents a new grey-box state space model structure for nonlinear systems together with its identification procedure. The model structure is linear in the input and a combination of a linear state space model and a feature space transformation. Because of the first two properties this model structure is interesting for control design. The identification procedure for the model structure assumes that all the state variables can be measured directly or accurately estimated from measurement data. The presented identification method is a combination of well established identification methods for linear and nonlinear black-box identification approaches, respectively linear least squares and neural training. Physical insight is required to first select the appropriate state variables, secondly to select the right type and number of features and finally to reduce the number of parameters and improve the model accuracy. Therefor this modeling can be considered as a grey-box approach. The modeling approach has been applied successfully on a quarter car test setup containing a nonlinear magneto-rheological semi-active damper.
IEEE Transactions on Aerospace and Electronic Systems, 2014
This paper concerns with the sensor management problem in collocated Multiple-Input Multiple-Outp... more This paper concerns with the sensor management problem in collocated Multiple-Input Multiple-Output (MIMO) radars. After deriving the Cramer-Rao Lower Bound (CRLB) as a performance measure, the antenna allocation problem is formulated as a standard Semi-definite Programming (SDP) for the single-target case. In addition, for multiple unresolved target scenarios, a sampling-based algorithm is proposed to deal with the non-convexity of the cost function. Simulations confirm the superiority of the localization results under the optimal structure.
2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012
ABSTRACT
2010 13th International Conference on Information Fusion, 2010
ABSTRACT
2010 IEEE Aerospace Conference, 2010
ABSTRACT
2014 IEEE Radar Conference, 2014
We consider waveform design for multiple-input, multiple-output radar systems for the case where ... more We consider waveform design for multiple-input, multiple-output radar systems for the case where the signal, during propagation, undergoes phase perturbations. We formulate an iterative algorithm to obtain both waveform parameters and the weights of the adaptive matched filter. An example of a clutter and target model is provided to show how the optimal waveform design improves the detection performance of a random-phase radar compared to traditional waveforms.
... Nariman Mahdavi, Ali A.Gorji, Mohammad B. Menhaj, and Saeedeh Barghinia ... A completely simi... more ... Nariman Mahdavi, Ali A.Gorji, Mohammad B. Menhaj, and Saeedeh Barghinia ... A completely similar manner can be also considered to compute Amerge and Asplit by computing p∗ K where K∗ is the new set of basis functions after merging or splitting old ones. ...
This paper deals with the antenna allocation problem in Multiple-Input Multiple-Output (MIMO) rad... more This paper deals with the antenna allocation problem in Multiple-Input Multiple-Output (MIMO) radar systems with collocated antennas. After deriving the Cramer-Rao Lower Bound (CRLB) as the cost function, the optimal distribution of antennas is found by applying the relevant operators to the CRLB. A convex optimization algorithm is then proposed to find the optimum distribution of antennas that achieves the optimal CRLB. It is also shown that the optimization problem can be simplified to the well-known Semi-definite Programming (SDP) for a single target scenario. Using a number of simulations, it is shown that the localization algorithm also leads to superior results when the optimal antenna configuration is used.
This paper concerns with the sensor management problem in collocated Multiple-Input Multiple-Outp... more This paper concerns with the sensor management problem in collocated Multiple-Input Multiple-Output (MIMO) radars. After deriving the Cramer-Rao Lower Bound (CRLB) as a performance measure, the antenna allocation problem is formulated as a standard Semi-definite Programming (SDP) for the single-target case. In addition, for multiple unresolved target scenarios, a sampling-based algorithm is proposed to deal with the non-convexity of the cost function. Simulations confirm the superiority of the localization results under the optimal structure.
2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 2008
Page 1. Artificial Neural Networks for Stochastic Control of Nonliner State Space Systems Ali A.G... more Page 1. Artificial Neural Networks for Stochastic Control of Nonliner State Space Systems Ali A.Gorji, Student Member, IEEE, and Mohammad B.Menhaj, Member, IEEE Abstract In this paper, stochastic control of nonlinear state space models is discussed. ...
2012 15th International Conference on Information Fusion, 2012
This paper deals with the antenna allocation problem in Multiple-Input Multiple-Output (MIMO) rad... more This paper deals with the antenna allocation problem in Multiple-Input Multiple-Output (MIMO) radar systems with collocated antennas. After deriving the Cramer-Rao Lower Bound (CRLB) as the cost function, the optimal distribution of antennas is found by applying the relevant operators to the CRLB. A convex optimization algorithm is then proposed to find the optimum distribution of antennas that achieves the optimal CRLB. It is also shown that the optimization problem can be simplified to the well-known Semi-definite Programming (SDP) for a single target scenario. Using a number of simulations, it is shown that the localization algorithm also leads to superior results when the optimal antenna configuration is used.
14th International Conference on Information Fusion, 2011
Performance evaluation is one of the most important steps in any target tracking problem. The obj... more Performance evaluation is one of the most important steps in any target tracking problem. The objective of this paper is to present a brief review of different approaches available for the performance analysis of multiple target tracking algorithms. Metrics are first classified into sensor-dependent and tracker-dependent ones. Then, the tracker-dependent measures are discussed after classifying into two groups named algorithm-free and algorithm dependent measures. For the classification purposes, three different categories of algorithm-free metrics are described. Finally, to demonstrate the application of the metrics in evaluating the performance of different tracking algorithms, a challenging scenario is considered.
2018 52nd Asilomar Conference on Signals, Systems, and Computers, 2018
Tracking multiple moving targets with bearing-only measurement is a challenging task, due to the ... more Tracking multiple moving targets with bearing-only measurement is a challenging task, due to the inherent difficulties in determining the correct trajectory of the observer that will meet observability conditions. The work presented here formulates Observer Trajectory Planning (OTP) as a continuous control problem, and proposes reinforcement learning as a solution. The proposed architecture in this work constitutes a model-independent framework that allows for the estimation of the states of targets, and that allows multiple targets to be tracked in a realistic scenario, where the agent has no prior information about the initial locations and velocities of the targets.
This thesis concerns with the localization, tracking, and sensor management in the Multiple-Input... more This thesis concerns with the localization, tracking, and sensor management in the Multiple-Input Multiple-Output (MIMO) radar systems. The collocated and widely-separated MIMO radars are separately discussed and the signal models are derived for both structures. The first chapter of the thesis is dedicated to the tracking and localization in collocated MIMO radars. A novel signal model is first formulated and the localization algorithm is developed for the derived signal model to estimate the location of multiple targets falling in the same resolution cell. Furthermore, a novel tracking algorithm is proposed in which the maximum bound on the number of uniquely detectable targets in the same cell is relaxed. The performance of the tracking and localization algorithms is finally evaluated using the tracking Posterior Cramer-Rao Lower Bound (PCRLB). After showing the impact of the antennas position on the localization CRLB, a novel sensor management technique is developed for the coll...
Signal and Data Processing of Small Targets 2009
ABSTRACT
2008 5th International Conference on the European Electricity Market, 2008
... Mutual information (MI) is very effective in evaluating the relevance of each input from the ... more ... Mutual information (MI) is very effective in evaluating the relevance of each input from the aspect of information theory. Lags (load of the previous hours) are considered as potential inputs. ... In using FSD for STLF, the normalization is based on the previous day data. ...
2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 2008
... Nariman Mahdavi, Ali A.Gorji, Mohammad B. Menhaj, and Saeedeh Barghinia ... A completely simi... more ... Nariman Mahdavi, Ali A.Gorji, Mohammad B. Menhaj, and Saeedeh Barghinia ... A completely similar manner can be also considered to compute Amerge and Asplit by computing p∗ K where K∗ is the new set of basis functions after merging or splitting old ones. ...
7th IFAC International Symposium on Advanced Control of Chemical Processes (2009), 2009
This paper presents a new grey-box state space model structure for nonlinear systems together wit... more This paper presents a new grey-box state space model structure for nonlinear systems together with its identification procedure. The model structure is linear in the input and a combination of a linear state space model and a feature space transformation. Because of the first two properties this model structure is interesting for control design. The identification procedure for the model structure assumes that all the state variables can be measured directly or accurately estimated from measurement data. The presented identification method is a combination of well established identification methods for linear and nonlinear black-box identification approaches, respectively linear least squares and neural training. Physical insight is required to first select the appropriate state variables, secondly to select the right type and number of features and finally to reduce the number of parameters and improve the model accuracy. Therefor this modeling can be considered as a grey-box approach. The modeling approach has been applied successfully on a quarter car test setup containing a nonlinear magneto-rheological semi-active damper.
IEEE Transactions on Aerospace and Electronic Systems, 2014
This paper concerns with the sensor management problem in collocated Multiple-Input Multiple-Outp... more This paper concerns with the sensor management problem in collocated Multiple-Input Multiple-Output (MIMO) radars. After deriving the Cramer-Rao Lower Bound (CRLB) as a performance measure, the antenna allocation problem is formulated as a standard Semi-definite Programming (SDP) for the single-target case. In addition, for multiple unresolved target scenarios, a sampling-based algorithm is proposed to deal with the non-convexity of the cost function. Simulations confirm the superiority of the localization results under the optimal structure.
2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012
ABSTRACT
2010 13th International Conference on Information Fusion, 2010
ABSTRACT
2010 IEEE Aerospace Conference, 2010
ABSTRACT
2014 IEEE Radar Conference, 2014
We consider waveform design for multiple-input, multiple-output radar systems for the case where ... more We consider waveform design for multiple-input, multiple-output radar systems for the case where the signal, during propagation, undergoes phase perturbations. We formulate an iterative algorithm to obtain both waveform parameters and the weights of the adaptive matched filter. An example of a clutter and target model is provided to show how the optimal waveform design improves the detection performance of a random-phase radar compared to traditional waveforms.
... Nariman Mahdavi, Ali A.Gorji, Mohammad B. Menhaj, and Saeedeh Barghinia ... A completely simi... more ... Nariman Mahdavi, Ali A.Gorji, Mohammad B. Menhaj, and Saeedeh Barghinia ... A completely similar manner can be also considered to compute Amerge and Asplit by computing p∗ K where K∗ is the new set of basis functions after merging or splitting old ones. ...
This paper deals with the antenna allocation problem in Multiple-Input Multiple-Output (MIMO) rad... more This paper deals with the antenna allocation problem in Multiple-Input Multiple-Output (MIMO) radar systems with collocated antennas. After deriving the Cramer-Rao Lower Bound (CRLB) as the cost function, the optimal distribution of antennas is found by applying the relevant operators to the CRLB. A convex optimization algorithm is then proposed to find the optimum distribution of antennas that achieves the optimal CRLB. It is also shown that the optimization problem can be simplified to the well-known Semi-definite Programming (SDP) for a single target scenario. Using a number of simulations, it is shown that the localization algorithm also leads to superior results when the optimal antenna configuration is used.
This paper concerns with the sensor management problem in collocated Multiple-Input Multiple-Outp... more This paper concerns with the sensor management problem in collocated Multiple-Input Multiple-Output (MIMO) radars. After deriving the Cramer-Rao Lower Bound (CRLB) as a performance measure, the antenna allocation problem is formulated as a standard Semi-definite Programming (SDP) for the single-target case. In addition, for multiple unresolved target scenarios, a sampling-based algorithm is proposed to deal with the non-convexity of the cost function. Simulations confirm the superiority of the localization results under the optimal structure.