A performance analysis of a new periodogram for spectrum sensing

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).

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.

This report describes the hardware system and the set of algorithms we have developed for detecting damage in cables for the Advanced Development and Process Technologies (ADAPT) Program. This program is part of the W80 Life Extension Program (LEP). The system could be generalized for application to other systems in the future. Critical cables can undergo various types of damage (e.g. short circuits, open circuits, punctures, compression) that manifest as changes in the dielectric/impedance properties of the cables. For our specific problem, only one end of the cable is accessible, and no exemplars of actual damage are available. This work addresses the detection of dielectric/impedance anomalies in transient time domain reflectometry (TDR) measurements on the cables. The approach is to interrogate the cable using time domain reflectometry (TDR) techniques, in which a known pulse is inserted into the cable, and reflections from the cable are measured. The key operating principle is that any important cable damage will manifest itself as an electrical impedance discontinuity that can be measured in the TDR response signal. Machine learning classification algorithms are effectively eliminated from consideration, because only a small number of cables is available for testing; so a sufficient sample size is not attainable. Nonetheless, a key requirement is to achieve very high probability of detection and very low probability of false alarm. The approach is to compare TDR signals from possibly damaged cables to signals or an empirical model derived from reference cables that are known to be undamaged. This requires that the TDR signals are reasonably repeatable from test to test on the same cable, and from cable to cable. Empirical studies show that the repeatability issue is the 'long pole in the tent' for damage detection, because it is has been difficult to achieve reasonable repeatability. This one factor dominated the project. The two-step model-based approach is summarized as follows: Step 1, Cable Modeling: Given input-output TDR signals s(n) and x{sub U}(n) for a cable known to be free of damage, system identification algorithms are used to compute a dynamic prediction-error cable model that has output {cflx x}{sub U}(n). The model is declared valid when the innovations e{sub U}(n) = x{sub U}(n) {cflx x}{sub U}(n) satisfy a statistical zero-mean whiteness test. This validated model output is then used as a known reference to which other cables can be compared. Step 2, Cable Testing: The TDR output signal x{sub D}(n) from a cable under test is compared with the model output {cflx x}{sub U}(n) by computing the innovations e{sub D}(n) = x{sub D}(n) {cflx x}{sub U}(n). The innovations are tested using a short-term whiteness test statistic, which employs a statistical confidence interval. If the cable passes the test, this implies that the model is valid and the cable is declared undamaged. If the cable fails the test, this indicates a model mismatch, which means that the cable's dielectric properties have changed; and this implies that the cable is damaged. The test threshold is adjusted to maximize probability of detection and minimize probability of false alarm according to an empirically determined receiver operating characteristic (ROC) curve. An associated confidence interval on the probability of correct classification is also provided. The effectiveness of the algorithm is demonstrated using measured TDR signals for undamaged and damaged cables. Experimental and algorithmic methods for coping with repeatability issues are presented. The model-based damage detection algorithms have been shown to perform well for some representative examples of real TDR signals acquired using the two-dimensional (2D) mockup fixture. If the damage causes a short circuit, then damage detection performance is generally good to excellent. Examples include the cases demonstrated in this report for cuts and pinholes. If the damage does not cause a short circuit, then damage detection performance is generally poor to fair. Examples include the cases demonstrated in this report for crushes and partial cuts.

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.

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.

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.

Cognitive radio is considered as a promising technique to increase the utilization of limited spectral resource. The key issue in cognitive radio is to design a reliable spectrum sensing method that is able to detect the signal in the target channel as well as to recognize different signals. In this paper, focusing on classifying different OFDM signals, we propose a two-step detection and identification approach. The key parameters to separate different OFDM signals are the subcarrier spacing and guard interval. A simple but reliable peak detection method is adopted in the first step, while a peak searching method is used to determine the length of guard interval. Simulations are carried out in AWGN to verify the validation of the proposed method. It is shown that our method can satisfy the detection and identification requirement with a low false alarm probability.

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.

For the landmine detection problem, a detector that provides a high probability of detection and a lowprobability of false alarm is needed. It is often the case that detectors satisfy one requirement at the cost ofpoor performance with regard to the other. Single sensors cannot achieve this goal, since every sensor hasits advantages and disadvantages when dealing with a large variety of landmines, from large metal-casedmines to small plastic-cased mines, etc. Thus, in this paper we consider two types of sensors, EMI andGPR. Time-domain EMI has been extensively used in the military and humanitarian demining. However, itis essentially a metal detector, thus, can detect mines with high metal content successfully, as well as metaldebris in the environment. This yields poor detection performance on mines with low metal content andhigh false alarm rate ifthe field was contaminated by metallic clutter. On the other hand, GPR is a potentialtool for landmine detection, since it can detect and identify subsurface anomalies. A GPR system with widefrequency band can achieve good resolution and adequately deep penetration for landmine detection. In ourprevious work, we have shown that Bayesian detection approach can be applied to EMI data and providepromising results. In this paper, we present results that indicate that statistical signal processing techniquescan improve performance over the conventional detection methods, which are usually based on the energypresent in the signal. Specifically, we consider data taken by the Coleman Research Corporation (CRC)Handheld Standoff Mine Detection System (HSTAMIDS) at Fort A. P. Hill, VA. and Yuma, AZ. Theactive system of the HSTAMIDS contains co-located metal detector (MD) and GPR sensors, which allowsus to fuse the data from the MD and GPR sensors. Thus, in addition to discussing individual sensor dataprocessing, we also present result of data fusion of both the MD and the GPR data using the HSTAMIDSsystem.

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.

Spectrum-aware devices and cognitive radios with wideband spectrum sensing will be an integral part of 5G or beyond wireless broadband. They must be fast and energy efficient for opportunistic dynamic access to the licensed spectrum. Compressed sensing (CS) methods can implement wideband sensing with reduced time and power consumption but are inaccurate at low SNR. Eigen methods are one of the best among non-CS methods but are high in computational cost. In this paper, we present a simple feature named the variance of multi-scale moving average (VMMA) that can be directly used as a decision statistic, discriminating signal from noise very accurately, even at a low signal-to-noise ratio (SNR). VMMA computes variance in a specific way over the entire band after comparing the short-term and long-term moving averages. Tests on experimental spectrum data and numerical simulations show that the proposed algorithms are not only fast but also have higher detection probability than the algorithms developed in the literature. Analytical expressions for the probability of detection and false alarm, along with the complexities of the algorithms, are also derived.

Spectroscopic analysis in the Terahertz frequency range, providing characteristic “signatures” for explosive and non-explosive materials, is proposed as an efficient and powerful tool for explosive identification. It is demonstrated that spectral responses of materials can be used as fingerprints that distinguish cyclotrimethylenetrinitramine (RDX) from other materials even with simple detectors and a limited number of available frequencies. Detection is performed using a modified least squares approach and multilayer perceptrons that operate on smoothed reflectance spectra. The performance of the detectors is evaluated through application to spectra of RDX and several common materials. A Receiver Operating Characteristic curve analysis demonstrates that our detectors exhibit the desirable properties of high probability of detection and low probability of false alarm.