Cable Damage Detection System and Algorithms Using Time Domain Reflectometry

Satellite Navigation Receiver Autonomous Integrity Monitoring (RAIM) is developed for detection and recognition of satellite faults using the redundant observation values of receiver itself. This article first makes a comparative analysis to the conventional and recently proposed RAIM fault detection and identification methods, and then points out that the conventional fault identification methods have such weakness as low fault detection probability and high false alarm probability in the situation of simultaneous multiple faulty satellites, while the recently proposed multi-satellite fault identification methods require excessive computation work though enjoy a higher detection probability. To solve these problems, a new RAIM multiple satellite fault detection and recognition method is proposed in this paper, which is characterized by high detection probability, low false alarm probability and low computation load, practicable for project implementation. Based on a simulation analysis, it turns out that this method has a higher detection probability without false alarm at all. This method requires so low computation work that it only needs 2 m times of least squares residuals fault detection and m times of fault identification in m (m > 0) faulty satellites situation, showing a very promising application value.

Much of the research into multipath detectionand mitigation has not considered the carrier phase delaybetween the line of sight (LOS) and reflected signals. Anew variable referred to as early late phase (ELP) hasrecently been proposed to exploit this phase difference. Ithas been found that in a receiver tracking the L1 GPSsignal, the probability of detecting multipath is lower whenthe carrier phase difference between the LOS and areflected signal is an integer multiple of p. Since thepseudorange error caused by the multipath’s presence is thehighest in this case, we propose to exploit the coexistenceof another GPS civilian signal, the L2C. We present ananalysis of ELP for the L1 and L2C signals, and a com-bination of both, for detecting multipath. The multipathdetection performance has been compared using probabil-ities of false alarm and detection. An ideal algorithmshould have lower probability of false alarm and higherprobability of detection. However, it has been found thatusing dual-frequency ELP increases both probabilities.Thus, receiver operator characteristics (ROC) curves, andthe area under the ROC curves, have been used for effec-tive comparison. It has been found that the L2C signalindividually gives worse performance than L1 because ofits weaker signal strength. However, the combination of L1and L2C gives the best overall performance, and thus it canbe claimed that ELP using dual-frequency receivers is amore effective approach for detecting multipath.Keywords Multipath GPS L2C ELP Dual-frequency receiverIntroductionThe last decade or so has seen an increase in applicationsof GPS in urban and indoor applications, which hasencouraged extensive research activity in the field ofmultipath detection and mitigation. Algorithms widelycited are narrow correlators (Dierendonck et al. 1992),double-difference residuals (DDR) (Townsend and Fenton1994), the vision correlator (Fenton and Jones 2005) andthe multipath estimating delay locked loop (MEDLL)(Townsend et al. 2000). Since a reflected signal reachesthe receiver by a relatively longer path, it is delayed intime and also has a carrier phase difference with respectto the line of sight (LOS) signal (Hatch et al. 2007;Mubarak and Dempster 2008). The above algorithms (andmany others) only use the magnitude of the correlatoroutputs and hence do not exploit this phase difference. Anew parameter, called the ‘‘early late phase (ELP)’’, hasrecently been proposed for multipath detection and esti-mation (Mubarak and Dempster 2007). ELP is the phasedifference between the early and late correlator outputs,where the phase of a correlator output is equal to theinverse tangent of the Q-channel output divided by theI-channel output. It is higher in the presence of multipathand has been shown to be a useful discriminator to detectthe presence of multipath (Mubarak 2008).This paper first provides a brief description of ELP,followed by its analysis in the presence of multipath for theL1 and L2C signals, and the combination of both. Multi-path detection performance is then compared using prob-abilities of false alarm and detection. ROC curves and the

Sea or ground clutter is one of the main bottleneck factors for target detection in skywave over-the-horizon radar (OTHR). Conventional methods can partly suppress clutter, however, they typically suffer from two shortcomings. First, weak targets beside or within the clutter tend to be submerged, which increases the probability of miss-detection. Second, a large amount of isolated clutter may be residual, which increases the probability of false alarm (PFA). In this article, a clutter suppression method based on joint low rank and group sparse representation (JLRGSR) is proposed to address these issues. The properties of OTHR echo signals are firstly analyzed, including the low rank of clutter in the range-slow time domain, the column sparsity of clutter in the range-Doppler (RD) domain, and the group sparsity of targets in the RD domain, based on which the objective function is constructed. It is observed that clutter is primarily characterized by the dual constraints of low rank and column sparsity. Furthermore, the group sparsity constraint helps maintain the target morphology's integrity and reduces isolated clutter residuals. The objective function is solved through the alternating direction method of multipliers to realize clutter suppression. Finally, the effectiveness and superiority of the JLRGSR, that is, higher probability of detection and lower probability of false alarm, are experimentally verified by simulation data and real data.

Jamming attacks target a wireless network creating an unwanted denial of service. 5G is vulnerable to these attacks despite its resilience prompted by the use of millimeter wave bands. Over the last decade, several types of jamming detection techniques have been proposed, including fuzzy logic, game theory, channel surfing, and time series. Most of these techniques are inefficient in detecting smart jammers. Thus, there is a great need for efficient and fast jamming detection techniques with high accuracy. In this paper, we compare the efficiency of several machine learning models in detecting jamming signals. We investigated the types of signal features that identify jamming signals, and generated a large dataset using these parameters. Using this dataset, the machine learning algorithms were trained, evaluated, and tested. These algorithms are random forest, support vector machine, and neural network. The performance of these algorithms was evaluated and compared using the probability of detection, probability of false alarm, probability of miss detection, and accuracy. The simulation results show that jamming detection based random forest algorithm can detect jammers with a high accuracy, high detection probability and low probability of false alarm.

Response surface methodology for damage detection using frequency and mode shape

From 2007 to 2017, the Environmental Security Technology Certification Program (ESTCP) sponsored a series of live-site demonstrations to assess the capabilities of newly developed advanced geophysical classification (AGC) technologies to detect and classify buried targets of interest (TOI) (unexploded ordnance (UXO), inert and surrogate munitions, seeds, and any other objects for which the site team agrees that removal is required). These technologies have been shown to detect TOI and reject clutter with a high probability of detection and low probability of false alarm, saving the DoD in remediation costs. Previous retrospective analyses summarized the capabilities of AGC to differentiate TOI from clutter without taking the size of the detected objects into account. The capability to predict the size of a buried object could allow the DoD to more appropriately set the minimum separation distance (MSD) in UXO remediation projects, thus saving more in remediation costs. We have therefore conducted a retrospective analysis of the ESTCP live-site demonstrations in order to determine the capabilities of AGC to inform the MSD.

The cognitive radio is considered a best solution for the limited spectrum resources problem. The periodogram based energy detection can be used for spectrum estimation in cognitive radio. It does not need any prior information about the primary signal. This paper presents a new periodogram by using the Discrete Cosine Transform (DCT). In addition, it analyses and compares the performance with raw periodogram. The result reveals the DCT based periodogram is better than the traditional one due to its low variance. Consequently, the proposed system has a high probability of detection with low probability of false alarm even in case of lower SNR.

One of the complex problems nowadays in communication systems is the lack of frequency spectrum. To solve this problem, cognitive radio is considered the best candidate that can opportunistically exploit the spectrum. The periodogram based spectrum sensing technique can be used to detect the spectrum in cognitive radio. It is a useful technique since does not need to prior information about the primary signal. In this paper, a new periodogram is presented using the Discrete Cosine Transform (DCT). Results are analyzed and compared with the current raw periodogram. It is observed that the DCT periodogram outperforms the raw technique in terms of probabilities of false alarm and detection, variance, and complexity. In addition, the lowest power of DCT coefficients can be removed without compromising the sensing performance. The proposed system shows high probability of detection with low probability of false alarm even in the case of low Signal-to-Noise Ratio (SNR).

In a cognitive radio network (CRN), secondary users (SUs) utilize primary users (PUs) licensed spectrum in an opportunistic manner. Spectrum sensing is of the utmost importance in CRN to find and use the available spectrum without harmful interference to the PUs. Conventionally, to implement spectrum utilization, SUs are required to sense the primary spectrum first and then transmit data on the available spectrum. In this paper, we propose a dedicated wireless spectrum sensing network (WSSN), eliminating sensing overhead from SUs with the aim of improving achievable throughput. With WSSN assistance, we eliminate sensing time from the SUs frame, hence increasing the transmission time, which maximizes the achievable throughput. Additionally, the sensing duration is increased by deploying a dedicated WSSN, decreasing the probability of false alarm and achieving a targeted high probability of detection. A low probability of false alarm increases the spectrum utilization, improving the achievable throughput, while a high detection probability ensures PUs protection. Moreover, the proposed technique also addresses hidden and exposed terminal problems along with smooth spectrum mobility. Finally, we provide simulation results to demonstrate the proposed techniques, effectiveness. In the results, we have compared the achievable throughput of the proposed scheme with that of conventional CRN.

Cooperative spectrum sensing plays an important role in cognitive radio networks since it improves the detection performance by exploiting spatial diversity. However, the cooperation among terminals also brings additional communication overhead. In this paper, overhead-throughput tradeoff issues are investigated in four scenarios namely (1) identical sensing channel and perfect reporting channel, (2) identical sensing channel and imperfect reporting channel, (3) different sensing channel and perfect reporting channel, (4) different sensing channel and imperfect reporting channel of each secondary user (SU). Taking the reporting overhead into consideration, a novel frame structure consisting of an initial subframe and M consecutive subframes, is proposed to maximize the achievable throughput of the secondary network. And for each scenario, the overhead-throughput tradeoff is formulated as an optimization problem with respect to the number of reporting SUs. A brute-force approach is then used to resolve such optimization problem. Given the optimal number of reporting SUs, a set of candidate SUs is then selected according to the probability of detection, the probability of false alarm and the probability of reporting error. Numerical results show that an optimal overhead-throughput tradeoff is achieved given the optimal number of reporting SUs. In addition, the probability of false alarm is shown to be the most important factor affecting the performance of achievable throughput within the secondary network because the lower probability of false alarm corresponds to the case that the secondary network can use the channel with a higher chance.

In this paper, a method for recognition of frequency-hopping spread spectrum (FHSS) signals from compressive measurements is proposed. Conventionally, the detection method based on energy or signal variance does not represent the signal type. Other energy signals may also be falsely detected as FHSS signals, especially in the case of dynamic spectrum sharing. Besides, full FHSS spectrum scanning also makes data storage and processing challenges. In this paper, we propose a compressive detection mechanism based on pattern recognition to alleviate the stress of wide-band signal processing and detection category ambiguity. A partial discrete Fourier transform (DFT) matrix is designed based on the maximum mutual information between the received signal and the compressive measurements. Besides, to improve detection performance, a Toeplitz matrix developed as a filter. The DFT matrix and Toeplitz matrix together construct the measurement kernel. We extract the frequency domain features of received signals and detect the FHSS signals by a KNN classifier. The simulation results demonstrate that our compressive detection system can effectively detect FHSS signals and is insensitive to other signals. Even at low SNR, high detection probability and low false alarm probability can be obtained.

In the infrared small target detection, the clutter formed by buildings, trees and protruding clouds is densely distributed and difficult to filter out. The hysteresis threshold detection algorithm utilizes the geometric features of small target to reduce false alarms. Images are filtered in multiple scales, the location and scale of the points of interest are extracted by non-maximum suppression. To determine the connection state of the focus and clutter, local gradient second-order origin moment is proposed to eliminate strong edges. The hysteresis threshold segmentation is performed to exclude stubborn false alarms and detect small targets. Experiments show that the proposed algorithm has a significant effect in removing false alarms, and achieves both the high detection probability and low false alarm probability.

Assessing the robustness of a sensor system and the related predictions is a key step in structural health monitoring (SHM). In this paper, the SHM system under consideration uses macro fiber composite (MFC) sensors to generate ultrasonic guided waves for inspection of a composite bolted joint. Bolt bearing failure is introduced to a target hole through tensile loading. The MFC sensor-actuators are configured in a circular array around the target hole and used to send and receive the ultrasonic waves from many different directions. This strategy facilitates a scattering matrix approach to identifying the most effective actuation and sensing angles for damage detection. Multiple interrogation wavelengths are also utilized to study the effect of the wavelength relative to the size of hole being monitored. However, various other factors are expected to impact the ultrasonic signal and therefore the damage detection results. Particularly, the position and orientation of each sensor is precisely measured through image processing techniques in order to quantify the effects of sensor misalignment. Finally, the sensitivity of the ultrasonic inspection technique to variation in each phenomenon is compared to better understand the most significant effects on damage detection performance.

A new method of detection of coding signals under the background of sea clutter is presented. The process of signal detection consists of three stages: modeling sea clutter signals based on chaos, one-step ahead prediction of chaotic signals and detection decision making. In this method, models of chaotic signals were created in the form of multi-layer perceptron neural networks, coded signals take on 13-element Barker code. The experiment results show detection of coding signal by using this method has higher detection probability and lower false alarm probability and good performance of the whole detection although with a low SNR. This method turned out to be very robust to different chaotic signals.

Damage detection is a challenging, complex, and at the same time very important research topic in civil engineering. Identifying the location and severity of damage in a structure, as well as the global effects of local damage on the performance of the structure are fundamental elements of damage detection algorithms. Local damage detection is essential for structural health monitoring since local damages can propagate and become detrimental to the functionality of the entire structure. Existing studies present several methods which utilize sensor data, and track global changes in the structure. The challenging issue for these methods is to be sensitive enough in identifYing local damage. Autoregressive models with exogenous terms (ARX) are a popular class of modeling approaches which are the basis for a large group of local damage detection algorithms. This study presents an algorithm, called Influence-based Damage Detection Algorithm (IDDA), which is developed for identification of local damage based on regression of the vibration responses. The formulation of the algorithm and the post-processing statistical framework is presented and its performance is validated through implementation on an experimental beam-column connection which is instrumented by dense-clustered wired and wireless sensor networks. While implementing the algorithm, two different sensor networks with different sensing qualities are utilized and the results are compared. Based on the comparison of the results, the effect of sensor noise on the performance of the proposed algorithm is observed and discussed in this paper.