When the background noise is large or the sound source is far away from the microphone array, the localization accuracy of target sound sources will decrease dramatically. In order to solve this problem, a localization method of multi-aperture acoustic array based on time difference of arrival(TDOA) is proposed. The multi-aperture acoustic array is constructed by two small sub microphone arrays, which is supposed to surpass the performance of the equivalent large array composed of all microphones. The positions of the target sound source in two local coordinates are separately estimated by the two sub microphone arrays based on the generalized trust-region sub-problem in the modified polar representation (GTRS-MPR) method. Through the space coordinate transformation, the estimated two positions of the target sound source in the two local coordinates can be transferred into one global coordinate. To optimize the final estimated position of the target sound source by the multi-aperture acoustic array, two spatial lines are formed between the reference microphones of the two sub-arrays and the estimated two sources' positions in the global coordinate, and a search algorithm of shortest distance point based on the least-square method is proposed to fuse the localization results of the two sub-arrays. A simulation and an experimental platform are set up to verify the proposed method. The results show that the proposed method has higher localization accuracy compared with the existing methods. Especially, the accuracy of range estimation is greatly improved, which will be more suitable for the far-field sound source localization.

Theories to design a three-dimensional superresolution filter (TDSF) for confocal microscopy are proposed that can obtain a globally optimal solution through linear programming. The designed TDSF is proved to be a phase-only element introducing a phase delay of 0 or π. Five design examples of the TDSF are presented to demonstrate the validity of these theories. Regardless of transverse superresolution, a curve of S ^eu(G _a ^±) defined as the maximum value of Strehl ratio S under the axial resolving power of G _a ^± is calculated to set the fundamental limits of axial optical superresolution. Finally, what is to our knowledge a novel analytic expression of S ^eu(G _a ^±) is deduced.

The actual target range is unknown during the radar search process, which suggests that an elaborate transceiver synthesis should be concerned, also considering various number of missed target echo samples. This paper focuses on the joint robust synthesis of the radar code and filter bank to improve target detection capability against target echo eclipsing and clutter interference. For several practical considerations, two robust design strategies for transceiver optimization are considered. A majorization-minimization (MM) approach (Section III) is developed to deal with the average signal to interference plus noise ratio (SINR) design problem considering energy and similarity requirements on the waveforms. Then, the MM technique is extended to the max-min case. An accelerated MM-based procedure (Section IV) is presented for solving the worst-case SINR design problem under energy and peak-to-average ratio (PAR) requirements. Both the proposed algorithms can be ensured to converge to a B(oulingand)-stationary point. Finally, simulation results display that two proposed optimization strategies realize better target detection performance and a higher convergence rate compared with the existing approaches.

Various microphone array geometries (e.g., linear, circular, square, cubic, spherical, etc.) have been used to improve the positioning accuracy of sound source localization. However, whether these array structures are optimal for various specific localization scenarios is still a subject of debate. This paper addresses a microphone array optimization method for sound source localization based on TDOA (time difference of arrival). The geometric structure of the microphone array is established in parametric form. A triangulation method with TDOA was used to build the spatial sound source location model, which consists of a group of nonlinear multivariate equations. Through reasonable transformation, the nonlinear multivariate equations can be converted to a group of linear equations that can be approximately solved by the weighted least square method. Then, an optimization model based on particle swarm optimization (PSO) algorithm was constructed to optimize the geometric parameters of the microphone array under different localization scenarios combined with the spatial sound source localization model. In the optimization model, a reasonable fitness evaluation function is established which can comprehensively consider the positioning accuracy and robustness of the microphone array. In order to verify the array optimization method, two specific localization scenarios and two array optimization strategies for each localization scenario were constructed. The optimal array structure parameters were obtained through numerical iteration simulation. The localization performance of the optimal array structures obtained by the method proposed in this paper was compared with the optimal structures proposed in the literature as well as with random array structures. The simulation results show that the optimized array structure gave better positioning accuracy and robustness under both specific localization scenarios. The optimization model proposed could solve the problem of array geometric structure design based on TDOA and could achieve the customization of microphone array structures under different specific localization scenarios.

Accurate time delay estimation is critical in sound source localization methods that rely on time difference of arrival. Background noise and reverberation often introduce errors in time delay estimation. Generalized cross-correlation (GCC) functions, paired with different weighting functions, can adapt to various sound field environments for time delay estimation. To create a highly accurate time delay estimation method suitable for universal sound field conditions, this paper proposes a novel approach, which involves training multi-class weighted generalized cross-correlation features using a convolutional neural network. Various weighted GCC functions are employed to extract time delay features for the same microphone pairs. These time delay features from multi-class weighted GCC are fused to create a feature matrix. The feature matrix is then input into a convolutional neural network composed of convolutional layers and fully connected layers for training and prediction. In the network, time delay estimation is achieved using two different methods: regression and classification, with mean squared error and cross-entropy serving as loss functions, respectively. The proposed method is tested and validated through simulation scenarios featuring various signal-to-noise ratios and reverberation conditions. Time delay estimation results are compared with recent state-of-the-art (SOTA) methods, assessing accuracy, root mean square error, and mean absolute error. The results demonstrate that the proposed method achieves an impressive 3.36% enhancement in overall delay estimation accuracy (within 10cm), reduces the absolute error by 11.53%, and significantly decreases the estimated root mean square error by 16.07% compared to existing SOTA methods. Furthermore, the proposed model offers the advantages of compact size and efficient computational performance when compared to existing methods. These findings underscore the exceptional comprehensive performance of the proposed model in sound source localization applications.