Reflected Signal Research Articles

Lattice Boltzmann method for radiative transfer in two-layered slab with graded-index and Fresnel reflecting surfaces

Analysis of transport of radiative transfer in an inhomogeneous two-layered slab with graded refractive indices and bounded by Fresnel's reflecting walls is carried out using the lattice Boltzmann method. The radiative intensity in each layer is found as a superposition of two radiative waves, which undergo multiple reflections and refractions at the boundaries and interface. The accuracy and efficiency of the methodology is first tested for steady-state problems with constant or variable refractive indices and for time-resolved reflectance and transmittance in a single layer with graded-index. The effect of the governing parameters is examined and the results show that these parameters significantly influence radiative transfer. Especially, the curvature of the incoming beam due to refractive index variation changes when considering the increasing or decreasing variation of the refractive index. The smaller refractive index gap at the interface induces smaller reflection and radiative intensity discontinuity at the interface, while increasing the scattering albedo has no significant effect on the transmittance signal but corresponds to increasing the reflectance signal which takes more time to show the steady state profile. Moreover, the developed LBM is an efficient, powerful, and accurate solver for radiative transport in inhomogeneous layered media with graded-index.

Neuromorphic computing with hybrid CNN–Stochastic Reservoir for time series WiFi based human activity recognition

Wi-Fi Channel State Information (CSI) based human activity recognition (HAR) which using channel disturbances caused by signal reflection is a novel way of environment sensing and motion recognition. The collected channels characteristics are heavily influenced by the environment, human activity patterns and subject’s weight and height. These signal variations reflected from body components are mainly affected by static multipath effects comprises random noise and behave differently in individuals, and thus an active field of research. To reach further for achieving automated real-time classification, lower computational cost and easy adaptability to hardware are necessary. In this work, a CSI-based HAR with hybrid framework, Convolutional Neural Network (CNN)-Stochastic Reservoir (SR) (CNN-SR) has been proposed, enabling a subject adaptable and more efficient hardware implementation with minimal computational complexity. A subcarrier correlation matrix is first computed and portrayed in image without preprocessing based on the reflection of the raw CSI signal induced by human activities at regular intervals, allowing visual observation of whole pattern changes. The time-based features are subsequently extracted through CNN and these feature arrays are then feed into SR which based on stochastic spiking neural network (SSNN) in simple cycle reservoir architecture for template matching. SR offers attractive power savings over typical von Neumann systems, by doing stochastic computations. The proposed method has also been demonstrated that is capable for HAR based on partially captured signals. The signal pattern of each segment can be observed in a single sight and then employed for person-to-person template recognition. This enables HAR with minimal computational complexity and solving the inter-person variability concerns. The results demonstrate that the proposed CNN-SR achieves impressive performance in recognizing human activities and surpasses existing models with an average accuracy of 93.49%.

Analysis of signal waveform from a midsize liquid argon detector

The midsize single-phase liquid argon prototype detector, operating at the surface laboratory, is designed to measure scintillation light emitted by the liquid argon (LAr). The detector employs 42 8-inch photomultiplier tubes (PMT) to collect the light. By analyzing the waveform of the signal, important detector characteristics such as the slow decay time constant that characterizes the purity of the liquid argon can be obtained. To describe the signal waveform, a model, which takes into account the liquid argon emission decay times together with the TPB re-emission process as well as the signal reflection effects, is used. The TPB re-emission process is introduced using a three-exponential time structure. Additionally, experimental results provide comprehensive validation for a post-peak hump structure, which is attributed to signal reflection.

Semantic Proximity Update of GNSS/INS/VINS for Seamless Vehicular Navigation Using Smartphone Sensors

The advancement of microelectromechanical systems (MEMS) and the Internet of Things (IoT) have enabled a wide range of applications based on smartphones. However, the existing navigation methods using these low-cost MEMS sensors cannot provide acceptable information for location-based applications in various environments. Their technical limitations, such as severe signal attenuation, reflections, blockages, error accumulation, and low quality of images degrade the performance of GNSS, INS, and camera. To mitigate these limitations, especially in indoor vehicle navigation, we first analyze the performance of the existing fusion algorithm, then we propose Semantic Proximity Update (SPU) based on a pre-trained model of real-time object detection to enhance the integration of Global Navigation Satellite System (GNSS), Inertial Navigation System (INS) and Visual Inertial Navigation System (VINS). SPU consists of the detection of geo-referenced objects and the relative movement to infer the absolute position. The proposed INS/GNSS/VINS/SPU can maintain long-term acceptable accuracy regardless of the indoor/outdoor environment. It only requires the use of smartphone sensors; thus, this scheme has no additional cost for users. Experimental results indicated that the errors of this scheme in horizontal positioning and three-dimensional positioning were 51.6% and 86.8% lower, respectively than those of a conventional integration.

RIS-Based Opposite Phase Index Modulation and Its DNN-Assisted Joint Sequence Detection

Reconfigurable intelligent surface (RIS), a key technology for future wireless communications, allows intelligent reflection of signals and information transmission. The RIS-based modulation is considered a prospective information transmission mechanism. In this paper, a RIS-based opposite phase index modulation system is proposed, in which RIS can enhance the received power of user signals while transmitting additional data bits for near-end internet of things devices via opposite phase reflection pattern index without any additional radio frequency chain. To reduce the computational complexity of receiver detection, a deep neural network-assisted joint sequence detection method is proposed, in which the reflection pattern index and the symbol index are jointly detected. In addition, we derive the theoretical upper bound performance of the proposed modulation scheme in terms of the average bit error rate (BER) with the maximum likelihood detection. The theoretical and simulation results show that the proposed scheme has better BER performance and lower detection complexity than existing RIS-based modulation schemes.

Nonlinear Optical Response of an Excited 1/3 Laughlin Liquid

An ensemble of neutral excitations in a 1/3 Laughlin liquid is studied experimentally. It has been found that excitations induce a nonlinear optical response in the form of a quadratic dependence of the reflection signal on the pump power. The reported experimental results have shown that the observed effect is due to the contribution of the coherent anti-Stokes–Stokes scattering from the excited Laughlin liquid.

SATELLITE DERIVED BATHYMETRY FOR MONITORING NEARSHORE DYNAMICS

Research on the physical, ecological, and environmental processes in the coastal zone traditionally rely on the availability of accurate seafloor topography (bathymetry) information. This information becomes increasingly important as coastal environments are under pressure by climate change and anthropogenic developments. Nonetheless, the bathymetry of shallow nearshore waters is only marginally monitored by cost- and time-intensive survey techniques that are dependent on in-field environmental conditions. Here, Satellite Derived Bathymetry (SDB) offers a valuable alternative to enrich available bathymetric data in both data sparse and data rich environments, reducing the need for in-situ measurements in the future. The high spatial and temporal resolution of satellite imagery yields a more comprehensive understanding of our coast and its dynamics. Yet, consistently mapping temporal change is still challenging, as the majority of currently available empirical SDB algorithms are heavily dependent on in-situ data for calibration purposes. In this study we present a methodology for assessing nearshore horizontal morphodynamics using the uncalibrated reflectance signal from satellite sensors while focusing on the Friese Zeegat, a tidal inlet of the Dutch Wadden Sea.

Cooperative RIS and STAR-RIS Assisted mMIMO Communication: Analysis and Optimization

Reconfigurable intelligent surface (RIS) has emerged as a cost-effective and promising solution to extend the wireless signal coverage and improve the performance via passive signal reflection. Different from existing works which do not account for the cooperation between RISs or do not provide full space coverage, we propose the marriage of cooperative double-RIS with simultaneously transmitting and reflecting RIS (STAR-RIS) technologies denoted as RIS/STAR-RIS under correlated Rayleigh fading conditions to assist the communication in a massive multiple-input multiple-output (mMIMO) setup. The proposed architecture is superior since it enjoys the benefits of the individual designs. We introduce a channel estimation approach of the cascaded channels with reduced overhead. Also, we obtain the deterministic equivalent (DE) of the downlink achievable sum spectral efficiency (SE) in closed form based on large-scale statistics. Notably, relied on statistical channel state information (CSI), we optimise both surfaces by means of the projected gradient ascent method (PGAM), and obtain the gradients in closed form. The proposed optimization achieves to maximise the sum SE of such a complex system, and has low complexity and low overhead since it can be performed at every several coherence intervals. Numerical results show the benefit of the proposed architecture and verify the analytical framework. In particular, we show that the RIS/STAR-RIS architecture outperforms the conventional double-RIS or its single-RIS counterparts.

Design of Passive Transmission Line by Adjusting Intrinsic Parameters of Josephson Junctions

Along with the improvement of integration and the requirement of high-speed operation, the passive transmission line (PTL) plays a significant role in superconducting integrated circuits due to its intrinsic advantage of extremely low propagation delay and low signal loss in long-distance transmission. Good impedance matching is critical to reduce the signal reflection in the transmission line and maximize the power transferred. Based on the PTL wiring layer selection under SIMIT-Nb03 process, we studied the equivalent input/output impedance for Josephson junction (JJ) at the IO port and explored a method of impedance matching in rapid-single-flux-quantum (RSFQ) circuits by adjusting the Stewart McCumber parameter <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\beta_c$</tex-math></inline-formula> of the JJ in the design of the PTL's driver and receiver. Furthermore, the accompanying influence from the JJ's modified <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\beta_c$</tex-math></inline-formula> on the performance of the driver and receiver is evaluated to maximize the profit of the functional circuits. The common-used serial resistor is removed to obtain a balanced bias margin for the PTL circuits. The proposed PTL has been designed and tested based on SIMIT-Nb03 fabrication process. ±30% bias margin for the PTL is achieved at low frequency and high frequency (40 GHz) contrast tests, including operation speed and transmission length as test vector. Additionally, a frequency sweep for 1 mm PTL is performed by the designed ring oscillator on the chip. The maximum working frequency is up to 129 GHz, with a margin duration of 8%.

ANALYSIS THE IMPACT OF ENCLOSING VARIOUS MATERIALS ON THE STRENGTH OF WIFI SIGNAL RECEPTION

Installation Wi-Fi is sometimes hindered by obstacles such as wood, concrete, and glass. The presence of these materials may affect the signal strength received by the user. Therefore, research was carried out by enclosing router using PVC, acrylic and glass materials with a thickness of 2mm, 3mm and 5mm and a distance of 2m, 4m, 6m, 8m, 10m, 12m, 10.2m and 11.4m for RSSI measurements and Quality of Service at a distance of 12m and 11.4m in the morning, afternoon and evening. The measurement results show a decrease in RSSI depending on the obstacle and distance. This is because the signal experiences diffraction and reflection which causes the signal strength to weaken and the farther the distance the weaker the signal. Measurements at the moment non-line of sight has quite poor results, especially when the glass barrier with a thickness of 5mm is at a distance of 11.4 with an average RSSI value of 74.6 dBm. QoS parameter results show Throughput When video streaming all have a bad category due to low number of packets. Parameter Packet Loss all of them are in a very good category, showing the least missing packets. Parameter Latency all of them have a very good category though Throughput low, influenced by the number of incoming packets. Parameter Jitter good with 2mm and 3mm barriers, while 5mm in the afternoon is moderate, influenced by Latency.

Preliminary inquiry on the linear relationship between the height of the station and the ground height error retrieved by GNSS-IR with low-cost smart electronic equipment

Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) ground height retrieval technology is based on global navigation satellite system (GNSS) signal reflection, which can achieve efficient and high-precision ground retrieval. However, errors cannot be avoided. And whether there is a linear relationship between the height of the station and the error is unknown. This research uses Hi-Target geodetic GNSS receivers, smart phone devices (Honor 60) and smart tablet devices (Huawei MatePad Pro) to collect a total of 5 d data from DOY65 to DOY69 in 2023, with the station heights of 0.8 m, 1.0 m, 1.2 m, 1.4 m and 1.6 m, respectively. The experimental results show that each satellite can effectively establish a linear relationship between the inversion error and the station height, which can be used in the error compensation research of different station heights under the limitation that the height of reflector is between 0.8 m and 1.6 m. Simultaneously, the error is related to the influence of comprehensive factors such as reflector type, satellite number, and data-receiving equipment. Secondly, two clustering methods, k-means and k-media, are introduced to cluster a and b in the linear relationship y = ax + b of each satellite, and it is proved that the linear relationship between inversion error and station height is obviously related to ground reflection surface (plastic track and concrete ground). Finally, it is verified that the height measurement accuracy of low-cost smart electronic equipment (Root Mean Square Error (RMSE): 0.047 m and 0.042 m) is worse than that of GNSS (RMSE: 0.010 m), but it still has good measurement performance. All in all, this study provides an essential technical reference for the error compensation of different station heights and for the application of GNSS-IR with low-cost smart electronic equipment. Due to its low-cost advantage, it has great potential in developing other surface parameter inversion of GNSS-IR technology.

Numerical simulation of ultrashort-pulse reflectometry (USPR) on EAST

The microwave reflectometer is a popular non-intrusive plasma density diagnostic instrument on tokamaks that provides centimeter and millisecond level resolution. The ultrashort-pulse reflectometer (USPR) achieves plasma density measurement by emitting a chirped wave containing a broadband signal and measuring the time of flight from different frequency components. A USPR system is currently being built on EAST (Experimental Advanced Superconducting Tokamak) to meet the needs of diagnostic of the pedestal density evolution, such as high-frequency small edge-localized modes. In order to predict the density reconstruction of the EAST USPR system, this work presents a numerical simulation study of the beam propagation of the chirped wave of extraordinary waves (X-mode) in the plasma based on Python. The electron density profile has been successfully reconstructed by the reflection signal interpretation. The small gap between the right-hand cut-off layer and the electron cyclotron resonance layer, due to the low plasma density on the plasma edge, causes unexpected leakage from the transmitting microwave beam to the pedestal and the core region. This kind of ‘tunneling’ effect will cause the reflected signal to have energy loss in the low-frequency band. The study also discusses the influence of the poloidal magnetic field on the reflected signal. The spatial variation of the poloidal magnetic field will lead to the conversion between extraordinary (X) waves and ordinary (O) waves, which leads to energy loss in the reflected signals. The simulation results show that the ‘tunneling’ effect and the O-X mode conversion effect have little effect on the EAST USPR system. Therefore, the currently designed transmit power meets the working requirements.

Prediction of the internal structure of a lithium-ion battery using a single ultrasound wave response

This paper describes a means to predict the internal structure of a lithium-ion battery from the response of an ultrasonic pulse, using a genetic algorithm. Lithium-ion batteries are sealed components and the internal states of the cell such as charge, health, and presence of structural defects are difficult to measure. Ultrasonic inspection of lithium-ion batteries is a recent and growing area of research. Reflected and transmitted ultrasound pulses are proposed as a non-invasive means of gaining insights into the internal structure and changes within the closed body of a cell. However, the multiple layers present in a lithium-ion cell are problematic when attempting to interpret waveforms as many internal reflections superimpose. Attributing specific features of a cell to wave characteristics is challenging.In this work a genetic algorithm has been developed as a means to reverse engineer a single ultrasound wave response to predict the internal layered structure of a lithium-ion battery cell. A first randomised guess at the layered structure is made. A numerical wave propagation model is used to predict the ultrasound waveform associated with that structure. This waveform is then compared with a measured or reference waveform to establish its fitness. The layered structure is generationally mutated until the predicted waveforms converge on the reference signal. As this occurs the predicted layered body reveals insights into the cell structure under inspection.Initially, the algorithm was tested against an idealised model battery and its predicted waveform, giving a model-model verification. Further, experimental ultrasonic reflection signals were captured from small capacity lithium-ion cells. Estimations of layer structure predicted by the model were compared with CT-scans of the cells to assess performance. The genetic algorithm was found to be effective in converging the predicted wave response to the reference signal and creating accurate battery structures. It was shown that only part of the waveform was required to generate accurate predictions, which is helpful in avoiding parts of the signal contaminated by near field transducer effects.It was demonstrated that the genetic algorithm can predict material wave speed to 40–1100 m/s (3–29 %) accuracy when battery layer geometry is provided; and thicknesses to within approximately 0.2–7.5 μm (1–13 %) when material properties are provided. Providing the genetic algorithm with parameter constraints; either the layer topology and/or the material properties, substantially improved predictions to estimate the wave speeds on average to approximately ±50 m/s (3–4 %) and the layer thicknesses ±5 μm (7–8 %).This raises the possibility of the use of this approach to predict state of charge when the battery construction is known, or the presence of internal defects and damage to a known battery material composition.

Improving the Pulse-Limited Footprint Resolution of GNSS-R Based on the Novel Joint Bandwidth Method

The bistatic global navigation satellite system’s (GNSS) signal reflection technology has become an effective means of space-based sea surface wind field retrieval and height retrieval. By adopting a wider signal bandwidth, a higher pulse-limited footprint resolution can be achieved. However, for the GNSS-Reflectometry (GNSS-R) system, its signal bandwidth is affected by the signal bandwidth of the GNSS satellite, which limits the further improvement of the pulse-limited footprint resolution. This article proposes a method based on the novel signal bandwidth joint principle to improve the resolution of GNSS-R pulse-limited footprints. Firstly, currently in-orbit GNSS-R satellites use the traditional single frequency band (TSFB) method, which is limited by the GNSS satellite’s signals and has a theoretical upper limit on its signal bandwidth. In response to this issue, this article proposes the novel joint bandwidth (NJBW) method (Galileo E5a and E5b signals) based on the auto-correlation function (ACF) signal ambiguity theory. The NJBW method reduces the main lobe width of the ACF of the GNSS-R signal by jointly processing the signals of E5a and E5b frequency bands, thus improving the pulse-limit footprint resolution of GNSS-R. Secondly, in order to verify the improvement effect of the novel joint bandwidth method on the pulse-limited footprint resolution of GNSS-R, this paper designs and fabricates an NJBW antenna verification prototype for the joint Galileo E5a and E5b frequency band and tests it in a microwave anechoic chamber. The test results indicate that the radio frequency (RF) bandwidth of the NJBW antenna validation prototype can cover both the frequency bands of E5a and E5b, making it suitable for use as the NJBW method for the GNSS-R receiving antenna. The bandwidth test values of the NJBW antenna validation prototype are consistent with the design values, which verifies the correctness of the NJBW antenna design model and further proves the feasibility of the NJBW method. Thirdly, based on the joint Galileo E5a and E5b frequency band signals, the NJBW method was applied to analyze the improvement effect of the pulse-limited footprint resolution. Compared to the TSFB method, the application of the NJBW method can increase the resolution of the GNSS-R pulse-limiting footprint by 1.73 times, which effectively improves the performance of the GNSS-R system. The NJBW method proposed in this article provides the theoretical method foundation and key technical support for sea surface wind field retrieval and height retrieval and the antenna design for the future high-precision and high pulse-limited footprint resolution GNSS-R sea surface wind field retrieval and height retrieval verification satellite.

Anti-Stokes–Stokes Raman scattering of light from the excited Laughlin liquid

An ensemble of neutral excitations is constructed experimentally in the Laughlin liquid at the electron filling factor 1/3. The excitations are found to induce a nonlinear optical response, manifested as a quadratic dependence of the reflection signal on the excitation power. The reported experimental results indicate that the observed effect is due to the anti-Stokes–Stokes scattering of light from the excited Laughlin liquid.

Study of silicone hydrogel contact lenses’ surface reflection characteristics using confocal microscopy

The physical and chemical properties of contact lenses (CLs) differ significantly from one another. This is already covered by the FDA classification, which divides soft lenses into groups and subgroups for additional characteristics. The differences relate to both the interior and surface of the lens. Several differences in the surface characteristics of individual contact lenses have been studied and demonstrated to date. However, one of their fundamental physical properties, that is light reflection or, quantitatively, reflectance has not been compared. This paper describes the surface differences of a range of silicone-hydrogel (SiHy) lenses using reflectance confocal microscopy. It shows the relationship between the amount of light reflected from the lens surface and the material parameters. Common SiHy lens materials were used in the study, including two lenses with surface modifications. Light incident at the interface between two media (phosphate-buffered saline and lens) with different refractive indices is partially reflected. The normalized results show significant differences between the reflection signals (1 vs 0.07), and that they are not correlated with the refractive index (R2 = 0.5536). For the water content (%H2O), a general trend was observed that the higher the %H2O, the lower the reflection signal is (R2 = 0.8105). The reflection signal and surface modulus show the best correlation. (R2 = 0.9883). The proposed CLs analysis method, using reflectance confocal microscopy, provides data to differentiate between lenses with and without surface modifications.

Biorthogonal wavelet energy entropy method with different time windows for strong interference processing of GPR signals

In the light of the shielding effect of the strong reflection signal generated by the steel bars and the inhomogeneous of concrete materials on the weak signal in the tunnel lining detection, a high-resolution GPR image processing method of biorthogonal wavelet energy entropy in different time windows is proposed. Wavelet analysis has the characteristics of multiresolution, and wavelet entropy can represent the energy distribution characteristics of signals in different time windows with different scales. Firstly, the discrete biorthogonal wavelet transform is applied to decompose the original GPR image into subimages with different frequencies and different spatial local changes. Secondly, appropriate time windows are selected for wavelet entropy adaptive threshold processing of each subimage in the light of the different amplitude ratio of effective signal to strong interference in each subimage, and finally the reconstructed image after removing the strong reflection is spectrally whitened. Both the forward simulations and actual measurement results show that the method can effectively remove strong reflection interference, clear weak signals, and maintain the continuity of the reflection events, and compared to the conventional pulse deconvolution, predictive deconvolution and wavelet entropy methods with the same time window, this method has a more thorough interference removal effect, and the effective signal performance is more obvious, which proves the effectiveness and accuracy of the proposed method in improving the resolution of GPR image and highlighting weak signal.

Seismic Characterization of the Blue Mountain Geothermal Field

Subsurface characterization is crucial for geothermal energy exploration and production. Yet hydrothermal reservoirs usually reside in highly fractured and faulted zones where accurate characterization is very challenging because of low signal-to-noise ratios of land seismic data and lack of coherent reflection signals. We perform an active-source seismic characterization for the Blue Mountain geothermal field in Nevada using active seismic data to reveal the elastic medium property complexity and fault distribution at this field. We first employ an unsupervised machine learning method to attenuate groundroll and near-surface guided-wave noise and enhance coherent reflection and scattering signals from noisy seismic data. We then build a smooth initial P-wave velocity model based on an existing magnetotellurics survey result, and use 3D first-arrival traveltime tomography to refine the initial velocity model. We then derive a set of elastic wave velocities and anisotropic parameters using elastic full-waveform inversion, and obtain PP and PS images using elastic reverse-time migration. We identify major faults by analyzing the variations of seismic velocities and anisotropy parameters, and reveal mid- to small-scale faults by applying a supervised machine learning method to the seismic migration images. Our characterization reveals complex velocity heterogeneities and anisotropies, as well as faults, with a high spatial resolution. These results can provide valuable information for optimal placement of future injection and production wells to increase geothermal energy production at the Blue Mountain geothermal power plant.

Constraints on the structure of the oceanic crust of the Tamu Massif by teleseismic P-wave coda autocorrelation

The Tamu Massif, considered the biggest single volcano on Earth, was formed by the accumulation of enormous amounts of magma erupting to the surface. It is the largest and oldest seamount in Shatsky Rise, which is the third largest oceanic plateau on Earth. However, the formation mechanism of Tamu Massif is still controversial because evidence point to different formation hypotheses. In this paper, we applied the P-wave coda autocorrelation method and used the hydrophone waveform data acquired by the ocean bottom seismometer (OBS) deployed on Tamu Massif to constrain the oceanic crust, and these results provide new finding on the structure of the oceanic crust for Tamu Massif. We hope it can provide some implications to research the formation mechanism of Tamu Massif. These results show that some stations in Tamu Massif received reflection signals from shallower depths that are nearly parallel to the seafloor. We infer that in the shallow oceanic crust, there is a layer composed of alternating eruptions of dense, higher velocity massive lava and sparse, lower velocity pillow lava flows, which have less density and lower velocity compared to the lower oceanic crust, with a strong acoustic impedance contrasts between them and thus able to generate a reflection signal, which is observed in our autocorrelation results.

Defect detection and identification for aircraft cable insulation layer based on deep forest

Due to the special internal environment of aircraft, cable damage is inevitable, which usually starts from insulation layer defects and may cause major economic losses, and even seriously threaten the life of people on board. According to the above issue, a defect detection system for aircraft cable insulation layer based on ultrasonic guided waves (UGWs) is built in this paper. In order to ensure the complete coupling between the sensor and the insulation layer of the aircraft cable, the macro fiber composite (MFC) flexible sensor is employed in the system. Both simulation and experimental results show that this method can simultaneously monitor four different types (abrasion, cut, semi-stripping and full-stripping) of cable insulation layer defects at different locations on the same cable. The reflection signals of different types of defects are extremely similar and difficult to distinguish directly. In this paper, a classification method for four types of defects based on the deep forest method is proposed. This method requires a small sample size, and the classification performance is not affected by network structure and parameters. The recognition accuracy can reach 100%, avoiding the problem of traditional deep learning classification relying on a large number of samples and requiring parameter adjustment. The proposed method in this paper is proven to be able to effectively carry out online monitoring and accurately classify defect types, which has guiding significance for aircraft maintenance personnel to take corresponding measures in time.