Moving Target Detection Using Colocated MIMO Radar on Multiple Distributed Moving Platforms

Chen, Peng; Zheng, Li; Wang, Xiaodong; Li, Hongbin; Wu, Lenan

doi:10.1109/tsp.2017.2714999

Cited by 102 publications

(59 citation statements)

References 54 publications

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“…Artificial neural network (ANN) has always been one of the primary solutions for short‐term load forecasting. The latest development of neural networks, especially the deep learning method, has had a tremendous impact in the fields of computer vision, natural language processing, and speech recognition . Researchers fuse their understanding of different tasks into specific network structures rather than using a fixed shallow neural network structure.…”

Section: Artificial Neural Network Approaches and Model For Short‐termentioning

confidence: 99%

“…The latest development of neural networks, especially the deep learning method, has had a tremendous impact in the fields of computer vision, natural language processing, and speech recognition. [45][46][47][48][49] Researchers fuse their understanding of different tasks into specific network structures rather than using a fixed shallow neural network structure. Different building blocks, including CNN 50,51 and LSTM, make deep neural networks highly flexible and efficient.…”

Section: Artificial Neural Network Approaches and Model For Short-termentioning

confidence: 99%

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An effective deep learning neural network model for short‐term load forecasting

Wang

et al. 2020

Concurrency and Computation

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Summary Energy load forecasting plays an important role in the smart grid, which can affect the promoting energy production and consumption decision‐making processes. In this paper, the state‐of‐the‐art deep learning (DL) neural models are used in the short‐term load forecasting, including the multilayer perceptron (MLP), the convolutional neural network (CNN), and the long short‐term memory (LSTM). A novel loss function is proposed for the load forecasting, and two commonly used benchmarks are used to verify the validity of the proposed function. The simulation results show that the mean absolute percentage error (MAPE) of the proposed loss function is 19.63% lower than cross‐entropy and 2.34% lower than mean absolute error (MAE). We compared the mentioned neural networks in different aspects, and the results show that in energy load forecasting, CNN has superior performance than MLP and LSTM in terms of high accuracy and robustness to weather changes.

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Section: Artificial Neural Network Approaches and Model For Short‐termentioning

confidence: 99%

Section: Artificial Neural Network Approaches and Model For Short-termentioning

confidence: 99%

An effective deep learning neural network model for short‐term load forecasting

Wang

et al. 2020

Concurrency and Computation

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“…The bistatic co-prime arrays in the radar subsystem are adopted in both transmitter and receiver [16,[18][19][20][21][22][23]. The number of antennas in transmitter and receiver are N t + 2Q t − 1 (2 ≤ Q t < N t ) and N r + 2Q r − 1 (2 ≤ Q r < N r ), respectively.…”

Section: System Model For Radar Subsystemmentioning

confidence: 99%

“…where is adopted to control the estimation accuracy and can be usually set as = σ 2 w , and x denotes the sparse vector, the non-zero entries are the target scattering coefficients, and the positions of the non-zero entries indicate the DoA and DoD [16][17][18]24,25].…”

Section: System Model For Radar Subsystemmentioning

confidence: 99%

“…Especially for the fast moving vehicles with frequent inter-cell handover, such as the high speed train (HST), much more overhead is needed to process the inter-cell handover, which will lead to low data rate, e.g., less than 0.2 M bps for the GSM-R and around 2 M bps for the new-developed broadband wireless communication for HST in Japan, Taiwan and Europe. Therefore, in this paper, a radar-aided mmWave communication (RAMC) system is proposed to tackle the aforementioned issues [13][14][15][16][17]. As shown in Figure 1, there are two parts in the RAMC system: 1.…”

Section: Introductionmentioning

confidence: 99%

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DoA and DoD Estimation and Hybrid Beamforming for Radar-Aided mmWave MIMO Vehicular Communication Systems

Chen

Cao

He³

et al. 2018

Electronics

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Abstract:In millimeter wave (mmWave) communications, the feature of relatively large signal absorption and directional transmission render new challenges for wireless communications and signal processing. To further improve the performance of mmWave communications, a novel radar-aided mmWave communication (RAMC) approach is proposed, which can be used in vehicular communications. There are two parts in the proposed RAMC system, including the radar subsystem and the mmWave communication subsystem. In the radar subsystem, the bistatic co-prime multi-input and multi-output (MIMO) arrays are considered. With the radar antenna arrays, both the directions of departure (DoD) and the directions of arrival (DoA) are estimated. Additionally, the compressed sensing (CS)-based method is proposed to obtain the target positions. Using the estimated angle and position information, the channel estimation and feedback link of the mmWave communication subsystem can be eliminated. Moreover, a hybrid beamforming algorithm is proposed in the mmWave communication subsystem, which can overcome the shortage of the analog-only beamforming. Simulation results show that the better estimation performance can be achieved by the bistatic co-prime MIMO arrays than that by the traditional uniform linear arrays (ULA), and with the radar aided, the mmWave communication subsystem can reduce the beam search time, and the cell discovery time is improved significantly.

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Using Coupled Multilinear Rank-(L, L, 1) Block Term Decomposition in Multi-Static-Multi-Pulse MIMO Radar to Localize Targets

Yang

Gong

et al. 2019

Lecture Notes in Computer Science

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Moving Target Detection Using Colocated MIMO Radar on Multiple Distributed Moving Platforms

Cited by 102 publications

References 54 publications

An effective deep learning neural network model for short‐term load forecasting

An effective deep learning neural network model for short‐term load forecasting

DoA and DoD Estimation and Hybrid Beamforming for Radar-Aided mmWave MIMO Vehicular Communication Systems

Using Coupled Multilinear Rank-(L, L, 1) Block Term Decomposition in Multi-Static-Multi-Pulse MIMO Radar to Localize Targets

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