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The inverse beamforming (IBF) is a mature method to improve azimuth resolution. However, for weak targets it is not applicable as IBF enhances side lobes. In this paper, an improved IBF algorithm is proposed to raise the azimuth resolution under the premise of ensuring the detection ability for weak targets. Firstly, from the point of phase compensation, we analyze the cause of side lobes when IBF is applied. Then the improved IBF algorithm recorded as GIBF (the improved inverse beamforming) is proposed by changing the Toeplitz average into the phase construction. The theoretical derivation and simulation data processing show the proposed method can improve the resolution of the N sensors to the standard of 2N − 1 sensors under different signal-to-noise ratios. Compared with IBF, GIBF has great advantages in detecting weak targets. Passive sonar data are used to further verify the advantages of GIBF; the trajectories on azimuth history diagrams become clear, the azimuth resolution is improved, and the detection ability for weak targets is still robust. In addition, GIBF is combined with the common DOA (direction of arrival) estimation algorithms, such as conventional beamforming and minimum variance distortionless signal response, which proves the applicability of the algorithm.

Radio tomography is a method for imaging a small scene by measuring the attenuation by objects of all links in a dense wireless sensor network. Multipath reduces the amount of attenuation a user induces on the links, corrupting the image. This paper introduces a beamforming method that does not induce latency in the image frame rate yet allows beamforming using transmit and receive weights on every link. Simulations demonstrate that the beamformer can successfully mitigate multipath, restoring the image to a nearly multipath-free quality. [ABSTRACT FROM AUTHOR]

• Integration of photoacoustic imaging on clinical ultrasound platforms is verified. • The laser source and the ultrasound platform are automatically synchronized. • Estimation of the line trigger frequency in the ultrasound platform is proposed. • Asynchronous synthetic aperture inverse beamforming is introduced. We present a proof-of-concept of an automatic integration of photoacoustic (PA) imaging on clinical ultrasound (US) imaging platforms. Here we tackle two critical challenges: the laser synchronization and the inaccessibility to the beamformer core embedded in commercial US imaging platform. In particular, the line trigger frequency (LTF) estimation and the asynchronous synthetic aperture inverse beamforming (ASAIB) were developed and evaluated in both k-Wave simulation and phantom experiment. The proposed method is an economical solution to enable PA imaging on a greater number of US equipment to further thrive the PA imaging research community. [ABSTRACT FROM AUTHOR]

The inverse beamforming (IBF) is a mature method to improve azimuth resolution. However, for weak targets it is not applicable as IBF enhances side lobes. In this paper, an improved IBF algorithm is proposed to raise the azimuth resolution under the premise of ensuring the detection ability for weak targets. Firstly, from the point of phase compensation, we analyze the cause of side lobes when IBF is applied. Then the improved IBF algorithm recorded as GIBF (the improved inverse beamforming) is proposed by changing the Toeplitz average into the phase construction. The theoretical derivation and simulation data processing show the proposed method can improve the resolution of the N sensors to the standard of 2N − 1 sensors under different signal-to-noise ratios. Compared with IBF, GIBF has great advantages in detecting weak targets. Passive sonar data are used to further verify the advantages of GIBF; the trajectories on azimuth history diagrams become clear, the azimuth resolution is improved, and the detection ability for weak targets is still robust. In addition, GIBF is combined with the common DOA (direction of arrival) estimation algorithms, such as conventional beamforming and minimum variance distortionless signal response, which proves the applicability of the algorithm. [ABSTRACT FROM AUTHOR]