This paper considers the problem of multiple unknown emitters tracking based on distributed sensor arrays. The most common two-step methods firstly track the intermediate parameters and then solve emitter positions. H...
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This paper considers the problem of multiple unknown emitters tracking based on distributed sensor arrays. The most common two-step methods firstly track the intermediate parameters and then solve emitter positions. However, the two-step methods are suboptimal as ignoring the matching information. In this paper, we introduce the novel thought of Direct Position Determination (DPD) to the tracking problem and propose the Non-homogeneous Data Fusion and Fast Position Update (NDFFPU) algorithm. Firstly, the NDFFPU algorithm adapts to tracking scenarios by reducing the weights of past data in autocorrelation estimation. Thereafter, the non-homogeneity error of received data is eliminated in the difference co-array domain, which reduces the sensitivity to observation position. Finally, the NDFFPU algorithm fuses all difference array data and performs the Newton iteration to update the emitter positions with high speed. The NDFFPU algorithm avoids the drawbacks of both the intermediate parameter estimation and the complex peak-searching. Extensive numerical simulations demonstrate the superiority of the NDFFPU algorithm in utilized array aperture, computational complexity, tracking accuracy, and robustness.
A rapid increase in popularity of smart devices equipped with acoustic sensors enables the design of speech enhancement methods for distributed setups. In this work, we present a distributed noise reduction scheme in ...
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ISBN:
(纸本)9781728119465
A rapid increase in popularity of smart devices equipped with acoustic sensors enables the design of speech enhancement methods for distributed setups. In this work, we present a distributed noise reduction scheme in which the time-frequency masks and spatial filters are estimated at the nodes based on the locally available microphone signals and the compressed signals received from other nodes, where each node transmits only a single signal. The proposed block processing facilitates online estimation of masks and distributed spatial filters in an interchanged fashion. The results of performed numerical experiments indicate that the proposed online distributed noise reduction scheme performs similarly to the centralized approach, in which signals of all microphones of the distributedarrays are available for joint processing, and it significantly outperforms the local approach in which only the local microphone signals are available for the estimation of masks and spatial filters.
A new method is proposed for the source location with distributedsensor array. Firstly, the direction of arrival (DOA) of each subarray is estimated by the Multiple Signal Classification (MUSIC) group delay function....
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ISBN:
(纸本)9781467390989
A new method is proposed for the source location with distributedsensor array. Firstly, the direction of arrival (DOA) of each subarray is estimated by the Multiple Signal Classification (MUSIC) group delay function. Secondly, the initial rectangular region is established from the DOA estimation. Finally, the regional contraction is performed based on the larger steered response power. The centroid of the final region is the source location. The group delay from the phase information of MUSIC spectrum can enhance the resolution of the DOA estimation. The region reduction is iteratively completed and substantially shrunk based on the virtual points' larger steered response power. The simulation results show that the proposed method provides a more accurate source location with a stronger noise robustness than the traditional triangulation location method.
A new method is proposed for the source location with distributedsensor array. Firstly, the direction of arrival (DOA) of each subarray is estimated by the Multiple Signal Classification (MUSIC) group delay function....
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ISBN:
(纸本)9781467390996
A new method is proposed for the source location with distributedsensor array. Firstly, the direction of arrival (DOA) of each subarray is estimated by the Multiple Signal Classification (MUSIC) group delay function. Secondly, the initial rectangular region is established from the DOA estimation. Finally, the regional contraction is performed based on the larger steered response power. The centroid of the final region is the source location. The group delay from the phase information of MUSIC spectrum can enhance the resolution of the DOA estimation. The region reduction is iteratively completed and substantially shrunk based on the virtual points' larger steered response power. The simulation results show that the proposed method provides a more accurate source location with a stronger noise robustness than the traditional triangulation location method.
There has been considerable effort over the past few years to develop MANETs and to show their suitability for sensor networks. Concurrently, there has been vast improvement in radio link capability, primarily through...
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ISBN:
(纸本)9781424412112
There has been considerable effort over the past few years to develop MANETs and to show their suitability for sensor networks. Concurrently, there has been vast improvement in radio link capability, primarily through the adoption of MIMO antenna technologies. The combination of these elements has been proposed as a means to provide higher performance sensing capability, but models currently assume that only single nodes, or single nodes within clusters, have MIMO links. This paper describes an approach using cooperation between nodes to collaboratively create MIMO, diversity and beam formed antennas from the distributedsensor elements. The power trade off in creating such distributed intelligent adaptive antennas versus using single nodes with smart antennas is addressed. The routing issues associated with both approaches are also addressed. The benefits promised by cooperative intelligent adaptive antennas for sensor networks include better power management, longer distance communication and higher data rates.
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