The time delay estimation is widely used in wireless location field, and is the research emphasis in complex environment of this field. The current delay estimation algorithms can be classified as five methods of correlation, high-order statistics, self-adaption, maximum likelihood and subspace. However, the existing algorithms can hardly achieve an ideal performance in small sample(single snapshot) and low signal-to-noise ratio environment during wireless location. In order to solve the problem about the insufficiency of the current algorithms in the above conditions, many new methods have been introduced into the delay estimation problem. The compressed sensing sparse reconstruction method has been applied to the signal processing field as a newly-proposed algorithm in recent years. The delay estimation is realized by using the method of sparse reconstruction, in which the sparse representation of the signal is the premise. The rational construction of the measurement matrix and the design of the signal reconstruction algorithm are the core of correct estimation.The purpose of this article is to deal with the lack of measurement data in small sample(single snapshot) and low signal-to-noise ratio environment during wireless location. In the model of wireless location, the signal can be represented as a sparse matrix form by selecting suitable sparse representation matrix. The wireless multi-channel is measured in the time domain, the propagation delay varies with channel and the delay representation in the time domain is sparse, so that it can be directly constructed into the form of sparse signal. Since the necessary and the sufficient condition of the coefficient sparse matrix successfully reconstructed by the measurement matrix are the measurement matrix meeting the restricted isometry property(RIP). The orthogonal measurement matrix based on the steering vector by the method of Gram-Schmidt is proven to achieve the RIP. A novel sparse reconstruction algorithm based on backtracking filter is constructed to estimate the time delay. In order to guarantee that the first selection includes the optimal atom, several atoms are selected. And then the backtracking mechanism is introduced, and the selected atoms are approached by the method of the minimum square to sequence the obtained signals and select the optimal atom. Therefore, this method can be used to guarantee that the optimal atom is selected. The presented algorithm can achieve the delay estimation by using the corresponding relation between the time delay and the measurement matrix in a high precision. Furthermore, the Cramer-Rao bound(CRB) of this model is derived. Finally, simulations show that the proposed approach is suitable for small sample(single snapshot) and low signal-to-noise ratio environment. The proposed method can achieve a higher precision than Root-Music and improve performance at low complexity cost compared with OMP algorithm. The simulation result proves that the algorithm is stable and reliable.
In order to scientifically allocate rural landscape resources, reasonably plan rural tourism space, and ensure that the local characteristics of the countryside are not homogenized when carrying out rural landscape design, this paper studies rural landscape design strategies based on deep learning models. The extreme learning machine algorithm, DBN-RBM algorithm model and the improved DBN-DELM algorithm are the main technical means to obtain research data and parameter calibration results for tourism planning and development work, and the rural planning direction and planning theme is determined through the rural landscape design pre-analysis work. The data show that the main motives of tourists’ rural experience tourism are close to nature 85.90% and leisure vacation 75%, followed by understanding culture 45.30%, novelty 30.70%, parent-child education 29.20%, health retreat 30.40%, and business meeting 5.90%. In this paper, the study of rural landscape planning and design can effectively alleviate the contradiction between people’s production and living and ecological environment and coordinate the benign development of rural and tourism elements in their respective spaces.
Time delay estimation (TDE) is a hot research topic in wireless location technology. Compressed sensing (CS) theory has been widely applied to image reconstruction and direction of arrival estimation since it was proposed in 2004. The sparse model can be constructed in time domain for estimating the time delay by using the CS theory. The measurement matrix plays a crucial role in the processing of signal reconstruction which is the core problem of CS theory. Therefore the research in the measurement matrix has becomes a hotspot in recent years. The existing measurement matrix is mainly divided into two categories, i.e., random measurement matrix and deterministic measurement matrix. The performance of random measurement matrix has bottlenecks. Firstly, because of the redundant measurement matrix data, the generation and storage of the random number put forward a high requirement for hardware. Secondly the random matrix can only satisfy the restricted isometry property in a statistical sense. The research of the deterministic measurement matrix is of great value under this background. The parity check matrix of low density parity check (LDPC) code has good performance in CS theory. However, the method of randomly selecting non-zero element position has a certain probability to generate a measurement matrix with a short loop structure during generating LDPC code measurement matrix. The robustness of the reconstruction performance decreases with the increase of iteration times. A novel quasi-cyclic CS algorithm based on progressive edge-growth is constructed to estimate the time delay. The purpose of this article is to deal with the need to store a large number of data in existing measurement matrix during time delay, by using the CS theory. The algorithm presented here can achieve TDE in a high precision. First, the theoretical bridge between CS and the maximum likelihood decoding is established. And the design criterion of measurement matrix based on the LDPC code is derived. The sparse measurement matrix with quasi-cyclic structure is constructed by introducing the idea of progressive edge-growth. Finally, the orthogonal matching pursuit algorithm is used to estimate the time delay. Furthermore, the computational complexity of the algorithm and the data storage of the measurement matrix are analyzed theoretically. Simulations show that the correct reconstruction probability of the proposed approach is higher than those of the Gauss random matrix and random LDPC matrix under the same dimension. Compared with the random LDPC matrix, the proposed method can improve performance at the expense of less complexity under the condition of the same data storage.
Abstract. In this paper, a novel method is constructed to estimate the time delay. The purpose of this article is to deal with the lack of measurement data in small sample (single snapshot) and low signal to noise ratio environment during wireless location. First, the sparse representation model of received signals is established. And then the measurement matrix is proofed to achieve the restricted isometry property. The idea of subspace pursuit is to find the subspace which consist of the received signal. Therefore, the delay estimation can be achieved using the corresponding relation between the time delay and the measurement matrix. Finally, simulations show that the subspace pursuit algorithm is suitable for small sample environment. The method can achieve a higher precision than greedy algorithms such as orthogonal matching pursuit and Regularized orthogonal matching pursuit algorithm. Furthermore, the subspace pursuit algorithm has a better performance in anti-multi channels.
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