Target Range Estimation Method using Ghost Target in the Submarine Linear Array Sonar

The performance of a direction-finding system is significantly degraded by the imperfection of an array. In this paper, the direction-of-arrival (DOA) estimation problem is investigated in the uniform linear array (ULA) system with the unknown mutual coupling (MC) effect. The system model with MC effect is formulated. Then, by exploiting the signal sparsity in the spatial domain, a compressed-sensing (CS)-based system model is proposed with the MC coefficients, and the problem of DOA estimation is converted into that of a sparse reconstruction. To solve the reconstruction problem efficiently, a novel DOA estimation method, named sparse-based DOA estimation with unknown MC effect (SDMC), is proposed, where both the sparse signal and the MC coefficients are estimated iteratively. Simulation results show that the proposed method can achieve better performance of DOA estimation in the scenario with MC effect than the state-of-the-art methods, and improve the DOA estimation performance about 31.64 % by reducing the MC effect by about 4 dB.

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Cost Modeling and Range Estimation for Top-k Retrieval in Relational Databases

Theoretical and Practical Advances in Information Systems Development ◽

10.4018/978-1-60960-521-6.ch012 ◽

2011 ◽

pp. 295-315

Author(s):

Anteneh Ayanso ◽

Paulo B. Goes ◽

Kumar Mehta

Keyword(s):

Relational Databases ◽

Estimation Method ◽

Synthetic Data ◽

Cost Modeling ◽

Data Sets ◽

Range Estimation ◽

Mapping Techniques ◽

The Cost ◽

Query Estimation ◽

Query Mapping

Relational databases have increasingly become the basis for a wide range of applications that require efficient methods for exploratory search and retrieval. Top-k retrieval addresses this need and involves finding a limited number of records whose attribute values are the closest to those specified in a query. One of the approaches in the recent literature is query-mapping which deals with converting top-k queries into equivalent range queries that relational database management systems (RDBMSs) normally support. This approach combines the advantages of simplicity as well as practicality by avoiding the need for modifications to the query engine, or specialized data structures and indexing techniques to handle top-k queries separately. This paper reviews existing query-mapping techniques in the literature and presents a range query estimation method based on cost modeling. Experiments on real world and synthetic data sets show that the cost-based range estimation method performs at least as well as prior methods and avoids the need to calibrate workloads on specific database contents.

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A Gridless Joint DOA and Range Estimation Method for FMCW MIMO Radar

Smart Communications, Intelligent Algorithms and Interactive Methods - Smart Innovation, Systems and Technologies ◽

10.1007/978-981-16-5164-9_38 ◽

2022 ◽

pp. 315-321

Author(s):

Jingyu Cong ◽

Tao Lu ◽

Zhang Bin ◽

Xiaoli Jing ◽

Xianpeng Wang

Keyword(s):

Estimation Method ◽

Mimo Radar ◽

Range Estimation

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A Study on the Robustness of Pitch-Range Estimation from Brief Speech Segments

International Journal of Asian Language Processing ◽

10.1142/s2717554520500034 ◽

2020 ◽

Vol 30 (01) ◽

pp. 2050003

Author(s):

Wenjie Peng ◽

Kaiqi Fu ◽

Wei Zhang ◽

Yanlu Xie ◽

Jinsong Zhang

Keyword(s):

Speaker Recognition ◽

Native Speakers ◽

Estimation Method ◽

Experimental Results ◽

Percentage Error ◽

Range Estimation ◽

Pitch Range ◽

Speech Segment ◽

The Mean ◽

Speech Segments

Pitch-range estimation from brief speech segments could bring benefits to many tasks like automatic speech recognition and speaker recognition. To estimate pitch range, previous studies have proposed to utilize deep-learning-based models with spectrum information as input. They demonstrated that such method works and could still achieve reliable estimation results when the speech segment is as brief as 300 ms. In this study, we evaluated the robustness of this method. We take the following scenarios into account: (1) a large number of training speakers; (2) different language backgrounds; and (3) monosyllabic utterances with different tones. Experimental results showed that: (1) The use of a large number of training speakers improved the estimation accuracies. (2) The mean absolute percentage error (MAPE) rate evaluated on the L2 speakers is similar to that on the native speakers. (3) Different tonal information will affect the LSTM-based model, but this influence is limited compared to the baseline method which calculates pitch-range targets from the distribution of [Formula: see text]0 values. These experimental results verified the efficiency of the LSTM-based pitch-range estimation method.

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