An Iterative Deconvolution-Time Reversal Method with Noise Reduction, a High Resolution and Sidelobe Suppression for Active Sonar in Shallow Water Environments

Abstract

Matched filtering is widely used in active sonar because of its simplicity and ease of implementation. However, the resolution performance generally depends on the transmitted waveform. Moreover, its detection performance is limited by the high-level sidelobes and seriously degraded in a shallow water environment due to time spread induced by multipath propagation. This paper proposed a method named iterative deconvolution-time reversal (ID-TR), on which the energy of the cross-ambiguity function is modeled, as a convolution of the energy of the auto-ambiguity function of the transmitted signal with the generalized target reflectivity density. Similarly, the generalized target reflectivity density is a convolution of the spread function of channel with the reflectivity density of target as well. The ambiguity caused by the transmitted signal and the spread function of channel are removed by Richardson-Lucy iterative deconvolution and the time reversal processing, respectively. Moreover, this is a special case of the Richardson-Lucy algorithm that the blur function is one-dimensional and time-invariant. Therefore, the iteration deconvolution is actually implemented by the iterative temporal time reversal processing. Due to the iterative time reversal method can focus more and more energy on the strongest target with the iterative number increasing and then the peak-signal power increases, the simulated result shows that the noise reduction can achieve 250 dB in the “ideal” free field environment and 100 dB in a strong multipaths waveguide environment if a 1-ms linear frequency modulation with a 4-kHz frequency bandwidth is transmitted and the number of iteration is 10. Moreover, the range resolution is approximately a delta function. The results of the experiment in a tank show that the noise level is suppressed by more than 70 dB and the reverberation level is suppressed by 3 dB in the case of a single target and the iteration number being 8.

Through-Wall UWB Radar Based on Sparse Deconvolution with Arctangent Regularization for Locating Human Subjects

“…Given the limitation of L2 norm regularization and the noisy characteristic of received signal y , we can easily estimate x to be a sparse signal (spike) from y by minimizing Equation (10) with a convex regularization term of the L1 norm [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ], . where is called L1 norm regularization (convex regularization) represented by the sum of absolute values of vector x , .…”

“…Basic deconvolution is the process of extracting the unknown input signal ( x ) of a linear time-invariant system ( y = Hx in matrix form) when the noise-free output signal ( y ) and wavelet ( H ) are known. However, in real-world applications, the output signal ( y ) is noisy and distorted by inhomogeneous media, such as ground-penetrating radar (GPR) [ 1 , 2 ], seismicity [ 3 , 4 ], radars [ 5 , 6 , 7 , 8 ], astronomy [ 9 ], speech recognition [ 10 , 11 ], and image reconstruction [ 12 , 13 , 14 , 15 ]. Nowadays, sparse deconvolution plays an important role in extracting the original data from the noisy received signal; it has been widely used in denoising, interpolation, super-resolution, and declipping [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ].…”

Pulse Ranging Method Based on Active Virtual Time Reversal in Underwater Multi-Path Channel

“…These methods attempt to minimize the effect of multi-path as it has a negative contribution to the ranging results. Time reversal (TR) [16,17] presents the opposite opportunity for its characteristics of compensating for multi-path effect and adaptive focusing.…”

Research on the Recognition Performance of Bionic Sensors Based on Active Electrolocation for Different Materials

“…The useful range of the sensor is usually within 30 km, and its detection performance is limited by the high-level side lobes and seriously degraded in a shallow water environment due to time spread induced by multipath propagation. Therefore, underwater environments pose significant challenges in identifying targets underwater [3,4]. The images collected by underwater camera equipment are not satisfactory because of the impact of light in turbid water.…”

“…The distance of sensor movement was calculated by calculating the time when the electrical signal image changed, and then the position information was obtained by using Equation (3). The calculated results (l) were compared with the actual measured results using Equation (2).…”