In this paper, we consider distributed signal processing in a MIMO radar network. We suppose that transmitting and receiving nodes of MIMO radar are distributed in a large-scale area. In this network the neighboring r...
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ISBN:
(纸本)9781538647523
In this paper, we consider distributed signal processing in a MIMO radar network. We suppose that transmitting and receiving nodes of MIMO radar are distributed in a large-scale area. In this network the neighboring receiving nodes communicate with each other to exchange data. A fully distributed algorithm for detection and imaging is proposed. This algorithm is based on the averagingconsensus approach. The effectiveness of the proposed technique is confirmed by numerical examples. It is compared to the centralized solution in a fusion center, the local solution in an individual receiving node and the proposed distributed algorithm.
We consider direction-finding in partly calibrated arrays composed of multiple identically oriented subarrays. The subarrays are assumed to possess a shift invariance structure that can be exploited for search free di...
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ISBN:
(纸本)9781479936878
We consider direction-finding in partly calibrated arrays composed of multiple identically oriented subarrays. The subarrays are assumed to possess a shift invariance structure that can be exploited for search free direction of arrival (DoA) estimation. We propose a fully distributed DoA estimation scheme that is based on the averaging consensus algorithm, in which the subarrays communicate only locally with their neighboring subarrays to exchange local averages of their measurements and received signal to iteratively compute global estimates in the network. The proposed scheme eliminates communication bottlenecks and the need for a centralized computation center. Our algorithm is based on subspace methods which is originally devised for DoA estimation in centralized systems. We show that in our fully distributed DoA estimation scheme, the number of jointly estimated directions can be larger than the number identifiable by each individual subarray.
This paper introduces a modified consensus-based real-time optimization framework for utility-connected and islanded microgrids scheduling in normal conditions and under cyberattacks. The exchange of power with the ut...
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This paper introduces a modified consensus-based real-time optimization framework for utility-connected and islanded microgrids scheduling in normal conditions and under cyberattacks. The exchange of power with the utility is modeled, and the operation of the microgrid energy resources is optimized to minimize the total energy cost. This framework tracks both generation and load variations to decide optimal power generations and the exchange of power with the utility. A linear cost function is defined for the utility where the rates are updated at every time interval. In addition, a realistic approach is taken to limit the power generation from renewable energy sources, including photovoltaics (PVs), wind turbines (WTs), and dispatchable distributed generators (DDGs). The maximum output power of DDGs is limited to their ramp rates. Besides this, a specific cloud-fog architecture is suggested to make the real-time operation and monitoring of the proposed method feasible for utility-connected and islanded microgrids. The cloud-fog-based framework is flexible in applying demand response (DR) programs for more efficiency of the power operation. The algorithm's performance is examined on the 14 bus IEEE network and is compared with optimal results. Three operating scenarios are considered to model the load as light and heavy, and after denial of service (DoS) attack to indicate the algorithm's feasibility, robustness, and proficiency. In addition, the uncertainty of the system is analyzed using the unscented transformation (UT) method. The simulation results demonstrate a robust, rapid converging rate and the capability to track the load variations due to the probable responsive loads (considering DR programs) or natural alters of load demand.
We consider direction-finding in partly calibrated arrays composed of multiple identically oriented subarrays. The subarrays are assumed to possess a shift invariance structure that can be exploited for search free di...
详细信息
ISBN:
(纸本)9781479936878
We consider direction-finding in partly calibrated arrays composed of multiple identically oriented subarrays. The subarrays are assumed to possess a shift invariance structure that can be exploited for search free direction of arrival (DoA) estimation. We propose a fully distributed DoA estimation scheme that is based on the averaging consensus algorithm, in which the subarrays communicate only locally with their neighboring subarrays to exchange local averages of their measurements and received signal to iteratively compute global estimates in the network. The proposed scheme eliminates communication bottlenecks and the need for a centralized computation center. Our algorithm is based on subspace methods which is originally devised for DoA estimation in centralized systems. We show that in our fully distributed DoA estimation scheme, the number of jointly estimated directions can be larger than the number identifiable by each individual subarray.
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