Real-time in-air acoustic imaging has long been challenged by the need to balance image quality, processing speed, and hardware complexity. Conventional beamforming methods often struggle to deliver clear, high-resolution images quickly enough for dynamic environments. Delay-Multiply-And-Sum (DMAS) beamforming emerges as a significant improvement by enhancing image contrast and resolution while maintaining feasible computational demands, thus pushing the boundaries of real-time acoustic imaging performance.
Short answer: Delay-Multiply-And-Sum beamforming improves real-time in-air acoustic imaging by providing better spatial resolution and contrast compared to traditional Delay-And-Sum methods, enabling clearer images with fewer artifacts and more effective noise suppression, all while supporting efficient real-time processing.
Traditional beamforming techniques in acoustic imaging, such as the widely used Delay-And-Sum (DAS) method, operate by delaying received signals from an array of sensors to align echoes from a target location and then summing them to form an image. While DAS is computationally straightforward and suitable for real-time applications, it suffers from limited spatial resolution and contrast. This limitation arises because DAS treats all signals linearly and equally, which can lead to image blurring and the presence of sidelobes—spurious signals from off-target directions—that degrade image quality.
Moreover, conventional methods often struggle with noise and multipath interference in in-air environments, where acoustic waves encounter reflections and scattering from objects and atmospheric conditions. These effects make it difficult to distinguish true targets from background noise, especially when imaging dynamic scenes requiring rapid updates.
Delay-Multiply-And-Sum beamforming enhances image formation by incorporating a nonlinear operation: after delaying signals to align echoes from a focal point, it multiplies pairs of delayed signals before summing them. This multiplication step effectively emphasizes coherent signals—those that are correlated and originate from the same spatial location—while suppressing incoherent noise and sidelobes that do not share consistent phase relationships across sensors.
This nonlinear correlation-based approach results in sharper point spread functions, improving spatial resolution beyond what linear DAS can achieve. The multiplication also enhances contrast, making targets stand out more clearly against the background. Importantly, DMAS does this without requiring significantly more complex hardware; it can be implemented efficiently on modern digital signal processors to support real-time imaging.
Comparative studies documented in the signal processing literature highlight that DMAS can reduce sidelobe levels by several decibels compared to DAS, effectively increasing the signal-to-noise ratio and improving image interpretability. These improvements are critical in real-time applications such as surveillance, non-destructive testing, and gesture recognition, where rapid and accurate imaging of objects in air is essential.
Real-Time Implementation and Computational Considerations
One might expect that the pairwise multiplication of signals in DMAS would impose a heavy computational burden, potentially hindering real-time performance. However, advances in algorithm optimization and parallel processing have mitigated these concerns. By leveraging efficient matrix operations and hardware acceleration (e.g., GPUs or FPGAs), DMAS beamforming can be executed with minimal latency.
Comparisons to conventional DAS implementations reveal that while DMAS requires more computation—roughly on the order of the square of the number of sensors versus a linear relationship for DAS—the benefits in image quality justify this cost. In practice, the trade-off is manageable for sensor arrays of moderate size, and the improved image fidelity can significantly reduce the need for post-processing or manual interpretation, saving time downstream.
Applications in In-Air Acoustic Imaging
In-air acoustic imaging presents unique challenges compared to underwater or medical ultrasound imaging due to lower acoustic impedance and higher environmental variability. DMAS beamforming’s enhanced noise suppression and resolution capabilities are particularly valuable here. For example, in security scanning, DMAS enables clearer detection of concealed objects by improving contrast between targets and clutter.
Researchers have demonstrated that DMAS outperforms DAS in identifying small or low-reflectivity targets in cluttered environments. This capability is crucial for applications like drone detection, gesture recognition in human-computer interaction, and environmental monitoring, where accurate, fast imaging can provide actionable information.
While some source domains like IEEE Xplore and ScienceDirect contain detailed technical analyses and experimental results on DMAS, others such as Springer Nature and Frontiers in Physics have limited accessible content on this exact topic currently. Nevertheless, the consensus in available literature supports DMAS as a superior beamforming method for real-time acoustic imaging tasks.
Takeaway
Delay-Multiply-And-Sum beamforming marks a significant step forward in real-time in-air acoustic imaging by combining nonlinear signal processing with practical computational strategies. It enhances image clarity and target detectability beyond conventional methods, enabling more reliable and faster acoustic visualization in complex environments. As digital processing power continues to grow, DMAS is poised to become a standard approach in applications demanding high-quality, real-time acoustic imaging.
For further reading and technical insights, reputable sources include IEEE Xplore for engineering-focused research, ScienceDirect for applied physics and signal processing studies, and technical overviews available through academic publishers such as Springer Nature.
