El procesamiento de señales por inversión temporal [ 1 ] es una técnica de procesamiento de señales que tiene tres usos principales: crear una señal portadora óptima para la comunicación, [ 2 ] reconstruir un evento fuente, [ 3 ] [ 4 ] [ 5 ] [ 6 ] y enfocar ondas de alta energía en un punto del espacio. Un espejo de inversión temporal (TRM) es un dispositivo que puede enfocar ondas utilizando el método de inversión temporal. Los TRM también se conocen como conjuntos de espejos de inversión temporal, ya que generalmente son conjuntos de transductores. Los TRM son bien conocidos y se han utilizado durante décadas en el dominio óptico. También se utilizan en el dominio ultrasónico.
Descripción general
Si la fuente es pasiva, es decir, un reflector aislado, se puede utilizar una técnica iterativa para concentrar la energía en ella. El TRM transmite una onda plana que se dirige hacia el objetivo y se refleja. La onda reflejada regresa al TRM, donde parece que el objetivo ha emitido una señal (débil). El TRM invierte y retransmite la señal como de costumbre, y una onda más concentrada se dirige hacia el objetivo. A medida que se repite el proceso, las ondas se concentran cada vez más en el objetivo.
Otra variante consiste en utilizar un único transductor y una cavidad ergódica . Intuitivamente, una cavidad ergódica permite que una onda que se origina en cualquier punto alcance cualquier otro punto. Un ejemplo de cavidad ergódica es una piscina de forma irregular: si alguien se zambulle, con el tiempo toda la superficie ondulará sin un patrón definido. Si el medio de propagación no presenta pérdidas y los límites son reflectores perfectos, una onda que se origina en cualquier punto alcanzará todos los demás puntos un número infinito de veces. Esta propiedad se puede aprovechar utilizando un único transductor y grabando durante un tiempo prolongado para obtener el mayor número posible de reflexiones.
Teoría
La técnica de inversión temporal se basa en una característica de la ecuación de onda conocida como reciprocidad : dada una solución a la ecuación de onda, la inversión temporal (usando un tiempo negativo) de esa solución también es una solución. Esto ocurre porque la ecuación de onda estándar solo contiene derivadas de orden par. Algunos medios no son recíprocos (por ejemplo, medios con muchas pérdidas o ruido), pero muchos muy útiles lo son aproximadamente, incluyendo las ondas sonoras en el agua o el aire, las ondas ultrasónicas en el cuerpo humano y las ondas electromagnéticas en el espacio libre. El medio también debe ser aproximadamente lineal .
Time reversal techniques can be modeled as a matched filter. If a delta function is the original signal, then the received signal at the TRM is the impulse response of the channel. The TRM sends the reversed version of the impulse response back through the same channel, effectively autocorrelating it. This autocorrelation function has a peak at the origin, where the original source was. The signal is concentrated in both space and time (in many applications, autocorrelation functions are functions of time only).
Another way to think of a time reversal experiment is that the TRM is a "channel sampler". The TRM measures the channel during the recording phase, and uses that information in the transmission phase to optimally focus the wave back to the source.
Experiments
A notable researcher is Mathias Fink of École Supérieure de Physique et de Chimie Industrielles de la Ville de Paris. His team has done numerous experiments with ultrasonic TRMs. An interesting experiment[7] involved a single source transducer, a 96-element TRM, and 2000 thin steel rods located between the source and the array. The source sent a 1 μs pulse both with and without the steel scatterers. The source point was measured for both time width and spatial width in the retransmission step. The spatial width was about 6 times narrower with the scatterers than without. Moreover, the spatial width was less than the diffraction limit as determined by the size of the TRM with the scatterers. This is possible because the scatterers increased the effective aperture of the array. Even when the scatterers were moved slightly (on the order of a wavelength) in between the receive and transmit steps, the focusing was still quite good, showing that time reversal techniques can be robust in the face of a changing medium.
In addition, José M. F. Moura of Carnegie Mellon University has led a research team working to extend the principles of Time Reversal to electromagnetic waves,[8] and they have achieved resolution in excess of the Rayleigh resolution limit, proving the efficacy of Time Reversal techniques. Their efforts are focused on radar systems, and trying to improve detection and imaging schemes in highly cluttered environments, where Time Reversal techniques seem to provide the greatest benefit.
Applications
The benefit of time reversal signal processing is that one need not know any details of the channel. The step of sending a wave through the channel effectively measures it, and the retransmission step uses this data to focus the wave. Thus one doesn't have to solve the wave equation to optimize the system,[9] one only needs to know that the medium is reciprocal. Time reversal is therefore suited to applications with inhomogeneous media.
An attractive aspect of time reversal signal processing is the fact that it makes use of multipath propagation. Many wireless communication systems must compensate and correct for multipath effects. Time reversal techniques use multipath to their advantage by using the energy from all paths.
Fink imagines a cryptographic application based on the ergodic cavity configuration. The key would be composed of the locations of two transducers. One plays the message, the other records waves after they have bounced throughout the cavity; this recording will look like noise. When the recorded message is time reversed and played back, there is only one location to launch the waves from in order for them to focus. Given that the playback location is correct, only one other location will exhibit the focused message wave; all other locations should look noisy.
See also
References
- ↑Anderson, B. E., M. Griffa, C. Larmat, T.J. Ulrich, and P.A. Johnson, "Time reversal," Acoust. Today, 4 (1), 5-16 (2008). https://acousticstoday.org/time-reversal-brian-e-anderson/
- ↑B. E. Anderson, T. J. Ulrich, P.-Y. Le Bas, and J. A. Ten Cate, "Three dimensional time reversal communications in elastic media," J. Acoust. Soc. Am. 139(2), EL25-EL30 (2016).
- ↑Scalerandi, M., A.S. Gliozzi, B.E. Anderson, M. Griffa, P.A. Johnson, and T.J. Ulrich, "Selective source reduction to identify masked sources using time reversal acoustics," J. Phys. D Appl. Phys. 41, 155504 (2008).
- ↑Anderson, B.E., T.J. Ulrich, M. Griffa, P.-Y. Le Bas, M. Scalerandi, A.S. Gliozzi and P.A. Johnson, "Experimentally identifying masked sources applying time reversal with the selective source reduction method," J. Appl. Phys. 105(8), 083506 (2009).
- ↑Larmat, C.S., R.A. Guyer, and P.A. Johnson, "Time-reversal methods in geophysics," Physics Today 63(8), 31-35 (2010).
- ↑Anderson, B.E., M. Griffa, T.J. Ulrich, and P.A. Johnson, "Time reversal reconstruction of finite sized sources in elastic media," J. Acoust. Soc. Am. 130(4), EL219-EL225 (2011).
- ↑Mathias Fink. Acoustic Time-Reversal Mirrors. Topics Appl. Phys. 84, 17-43. (2002)
- ↑José M. F. Moura, Yuanwei Jin. "Detection by Time Reversal: Single Antenna", IEEE Transactions on Signal Processing, 55:1, pp. 187-201, January 2007
- ↑Parvasi, Seyed Mohammad; Ho, Siu Chun Michael; Kong, Qingzhao; Mousavi, Reza; Song, Gangbing (1 January 2016). "Real time bolt preload monitoring using piezoceramic transducers and time reversal technique—a numerical study with experimental verification". Smart Materials and Structures. 25 (8) 085015. Bibcode:2016SMaS...25h5015P. doi:10.1088/0964-1726/25/8/085015. ISSN 0964-1726. S2CID 113510522.
External links
- Mathias Fink. Time Reversal of Ultrasonic Fields--Part 1: Basic Principles. IEEE Trans. Ultrasonics, Ferroelectrics, and Frequency Control. 39(5):pp 555--566. September 1992.
- Signal processing