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Airborne aircrafts are dependent on the Global Navigation Satellite System (GNSS), which is susceptible to interference due to the satellite base-station and cooperative communication. Synthetic aperture radar altimeter (SARAL) provides the ability to measure the topographic terrain for matching with Digital Elevation Model (DEM) to achieve positioning without relying on GNSS. However, due to the near-vertical coupling in the delay-Doppler map (DDM), the similarity of DDMs of adjacent apertures is high, and the probability of successful matching is low. To this end, a novel neural network of terrain matching and aircraft positioning is proposed based on the airborne SARAL imagery. The model-driven terrain matching and aircraft positioning network (TMP-Net) is capable of realizing aircraft positioning by utilizing the real-time DDMs to match with the DEM-based DDM references, which are generated by a point-by-point coupling mechanism between the airborne routine and ground terrain DEM. Specifically, the training dataset is established by a numerical simulation method based on a semi-analytical model. Therefore, DEM-based DDM references can be generated by forward deduction when only regional DEM can be obtained. In addition to the model-based DDM generation, feature extraction, and similarity measurement, an aircraft positioning module is added. Three different positioning methods are designed to achieve the aircraft positioning, where three-point weighting exhibits the best performance in terms of positioning accuracy. Due to the fact that both the weighted triplet loss and softmax loss are employed in a cooperative manner, the matching accuracy can be improved and the positioning error can be reduced. Finally, both simulated and measured airborne datasets are used to validate the effectiveness of the proposed algorithm. Quantitative and qualitative evaluations show the superiority. [ABSTRACT FROM AUTHOR]

Due to the lack of accurate labels for the airborne synthetic aperture radar altimeter (SARAL), the use of deep learning methods is limited for estimating the above ground level (AGL) of complicated landforms. In addition, the inherent additive and speckle noise definitely influences the intended delay/Doppler map (DDM); accurate AGL estimation becomes more challenging when using the feature extraction approach. In this paper, a generalized AGL estimation algorithm is proposed, based on a fully supervised altimetry enhancement network (FuSAE-net), where accurate labels are generated by a novel semi-analytical model. In such a case, there is no need to have a fully analytical DDM model, and accurate labels are achieved without additive noises and speckles. Therefore, deep learning supervision is easy and accurate. Next, to further decrease the computational complexity for various landforms on the airborne platform, the network architecture is designed in a lightweight manner. Knowledge distillation has proven to be an effective and intuitive lightweight paradigm. To significantly improve the performance of the compact student network, both the encoder and decoder of the teacher network are utilized during knowledge distillation under the supervision of labels. In the experiments, airborne raw radar altimeter data were applied to examine the performance of the proposed algorithm. Comparisons with conventional methods in terms of both qualitative and quantitative aspects demonstrate the superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Microwave induced thermoacoustic has attracted broad attention in recent years because of its potential for clinical noninvasive detection. The model matrix in the model-based reconstruction method is often constructed by simulation, assuming that the media is acoustically homogeneous. However, the thermoacoustic signal waves propagate through acoustically heterogeneous media in real imaging scenes and are recorded by sparse sensor array. Thus, there is a perturbation between the actual and pre-designed measurement matrix, called measurement matrix uncertainty. In this Letter, an improved model is proposed to reconstruct the sound pressure distribution from sparse signals with phase distortion caused by variations in the speed of sound between tissues. The measured thermoacoustic signals are employed to construct a series of complex signals, which contain amplitude and phase information. The signal model's sparse constraint of the estimated sound pressure map is combined with the nonzero constraint of the random measurements' phases with respect to the amplitude-only measures. Our simulation and experiment results indicate that the proposed model helps to improve the image quality reconstructed by sparse sampling in acoustically heterogeneous media. [ABSTRACT FROM AUTHOR]

Stroke is the third leading cause of mortality in many countries. Thermoacoustic imaging has the potential for stroke detection. However, some parameters in thermoacoustic imaging, such as speed of sound, are difficult to be obtained in advance, and a constant velocity value is assumed in the reconstructed algorithm. Thermoacoustic signals propagate through the soft brain tissue and the skull in actual stroke detection. This mismatch between the assumed and actual sound velocity will degrade the imaging quality. In this Letter, we propose a full waveform autofocus inversion method to reconstruct thermoacoustic images for stroke noninvasive and non-ionizing detection. Employing the difference between the simulation forward sensor signals and the measured signals, the approximate speed of sound distribution is updated continuously. The numerical simulation of a real human brain model and the experiment of a real human skull help us to validate the performance of the proposed method in clinical transcranial thermoacoustic detection. [ABSTRACT FROM AUTHOR]

The performance of the existing reconstruction algorithms based on compressive sensing (CS) in microwave induced thermoacoustic tomography (MITAT) is influenced by the positions of detectors. Besides, some a priori information, such as target distribution or the correlation among thermoacoustic signals, has not been taken into account. In this letter, a probabilistic reconstruction algorithm in MITAT based on sparse Bayesian learning is proposed. Different from norm-based point estimation algorithms in CS, the sound pressure distribution which needs to be estimated is provided by probability distributions in the probabilistic reconstruction algorithm and an image is reconstructed based on the posterior density. Compared with the widely used norm-based point estimation algorithms (GPSR, Lasso) whose solution is not always the sparsest, the sparse Bayesian learning framework is globally convergent which can produce the sparsest solution at the posterior mean. Therefore, the robustness of the probabilistic reconstruction is better than that of norm-based point estimation algorithms. In addition, the estimations of the initial pressure distributions can be more accurately provided if the correlation of thermoacoustic signals can be considered, especially under the condition of low signal to noise ratio (SNR). Simulations and experiments on real breast tumors demonstrate that the proposed algorithm improves the robustness of reconstruction and show better performance at low SNRs. [ABSTRACT FROM AUTHOR]

This paper presents a novel accurate array response control algorithm, abbreviated as \text A^2\text RC, and its application to array pattern synthesis. The proposed \text A^2\text RC algorithm deals with the problem of how to accurately control the array response at a given direction. Starting from the adaptive array theory, a deep analysis of the optimal weight vector is carried out. It is found that the normalized response at a given direction can be accurately adjusted to an arbitrary level, by means of making some simple modification to the initial weight vector. On this basis, all possible weight vectors, which have a specific form and can make the normalized response at the given direction equal to the prescribed value, are first figured out. Then, an effective approach to selecting the most appropriate one, which would cause the least pattern distortion, is devised. By applying the \text A^2\text RC algorithm, a new pattern synthesis approach for arbitrary arrays is developed. In this approach, the array pattern is adjusted in a point-by-point manner by successively modifying the weight vector. Contrary to the conventional approaches that assign artificial interferences in an ad hoc way, our approach is able to obtain the weight vector without iteratively determining the powers of the artificial interferences. Extensive simulation results are provided to demonstrate the performance of the \text A^2\text RC algorithm in array response control and the effectiveness of this algorithm in pattern synthesis under various situations. [ABSTRACT FROM PUBLISHER]