Search

This paper addresses polarimetric adaptive coherent detection of radar targets embedded in sea clutter. Initially, radar clutter data across multiple polarimetric channels is modeled using a compound Gaussian framework featuring an unspecified speckle covariance matrix and lognormal texture distribution. Subsequently, three adaptive polarimetric coherent detectors are derived, employing parameter estimation and two-step versions of the generalized likelihood ratio test (GLRT): the complex parameter Rao and Wald tests. These detectors utilize both clutter texture distribution information and radar data's polarimetric aspects to enhance detection performance. Simulation experiments demonstrate the superiority of three proposed detectors over the competitors, and that they are sensitive to polarimetric channel parameters such as secondary data quantity, target or clutter speckle correlation, and signal-to-clutter ratio disparity. Additionally, the proposed detectors exhibit a near-constant false alarm rate relative to average clutter power and speckle covariance matrix. [ABSTRACT FROM AUTHOR]

Constant false alarm rate (CFAR) detectors are developed to track changes in clutter intensities and to adapt the detection threshold to maintain a constant probability of false alarms. These adaptive thresholding mechanisms are initially intended when both target and clutter are exponentially distributed, and they degrade in performance when applied to newer target and clutter models. So, in application scenarios like Airborne Warning and Control Systems (AWACS) and ship remote sensing, when both the target and clutter are Pareto distributed, instead of the conventional way of tweaking the existing adaptive-thresholding CFAR detector, we pose the detection problem as a two-sample, Pareto vs. Pareto composite hypothesis testing problem. Considering no knowledge of both scale and shape parameters of Pareto distributed clutter, we derive the new adaptive-thresholding detector based on the generalized likelihood ratio test (GLRT) statistic. We further show that our proposed adaptive thresholding detector has a CFAR property and provide extensive simulation results to demonstrate the performance of the proposed detector. [ABSTRACT FROM AUTHOR]

Spoofing against the Global Navigation Satellite System (GNSS) is an attack with strong concealment, posing a significant threat to the security of the GNSS. Many strategies have been developed to prevent such attacks, but current detection methods based on signal direction for multi-agent spoofing require multiple antennas/receivers, leading to increased cost and complexity in implementation. Additionally, methods utilizing a moving single antenna cannot effectively detect multi-agent spoofing. Therefore, we introduce a novel spoofing-detection technique based on the intersection angle between two directions of arrival (IA-DOA) using a single rotating antenna. The essence of this approach lies in estimating the IA-DOA between a pair of signals by utilizing the carrier-to-noise ratio (CNR) and carrier phase single difference (CPSD) of the received signal. The estimation of IA-DOA should be consistent with the prediction when there is no spoofing. With spoofing, it is difficult to accurately simulate the directionality of navigation signals, which can disrupt the consistency between the estimation and prediction of IA-DOA. Therefore, estimations and predictions of IA-DOA can be used to establish detection variables through generalized likelihood ratio testing (GLRT) to detect multi-agent spoofing. We conducted a simulation to analyze the impact of the antenna's parameters on the detection performance and evaluated it through on-site experiments. The results indicate that the method proposed in this article can efficiently achieve real-time detection of multi-agent spoofing. [ABSTRACT FROM AUTHOR]

This paper addresses polarimetric adaptive coherent detection of radar targets embedded in sea clutter. Initially, radar clutter data across multiple polarimetric channels is modeled using a compound Gaussian framework featuring an unspecified speckle covariance matrix and lognormal texture distribution. Subsequently, three adaptive polarimetric coherent detectors are derived, employing parameter estimation and two-step versions of the generalized likelihood ratio test (GLRT): the complex parameter Rao and Wald tests. These detectors utilize both clutter texture distribution information and radar data’s polarimetric aspects to enhance detection performance. Simulation experiments demonstrate the superiority of three proposed detectors over the competitors, and that they are sensitive to polarimetric channel parameters such as secondary data quantity, target or clutter speckle correlation, and signal-to-clutter ratio disparity. Additionally, the proposed detectors exhibit a near-constant false alarm rate relative to average clutter power and speckle covariance matrix.

Spoofing against the Global Navigation Satellite System (GNSS) is an attack with strong concealment, posing a significant threat to the security of the GNSS. Many strategies have been developed to prevent such attacks, but current detection methods based on signal direction for multi-agent spoofing require multiple antennas/receivers, leading to increased cost and complexity in implementation. Additionally, methods utilizing a moving single antenna cannot effectively detect multi-agent spoofing. Therefore, we introduce a novel spoofing-detection technique based on the intersection angle between two directions of arrival (IA-DOA) using a single rotating antenna. The essence of this approach lies in estimating the IA-DOA between a pair of signals by utilizing the carrier-to-noise ratio (CNR) and carrier phase single difference (CPSD) of the received signal. The estimation of IA-DOA should be consistent with the prediction when there is no spoofing. With spoofing, it is difficult to accurately simulate the directionality of navigation signals, which can disrupt the consistency between the estimation and prediction of IA-DOA. Therefore, estimations and predictions of IA-DOA can be used to establish detection variables through generalized likelihood ratio testing (GLRT) to detect multi-agent spoofing. We conducted a simulation to analyze the impact of the antenna’s parameters on the detection performance and evaluated it through on-site experiments. The results indicate that the method proposed in this article can efficiently achieve real-time detection of multi-agent spoofing.

At present, there are many technical methods in the field of target detection, but the detection methods are greatly affected by direct-path signals and need technical support such as extracting pure direct-path signals, so they cannot be used under the condition of without-direct-path signals. In this paper, a distributed target detection method is studied under a without-direct-path signals system. In the case of without-direct-path signals, target detection is achieved by the generalized likelihood ratio test (GLRT). At the same time, the input echo signals in target detection need time synchronization, so the impact of time delay should be eliminated. However, the traditional time delay estimation method is realized through coherent processing between echoes, which requires a high signal-to-noise ratio (SNR). Therefore, a time delay estimation method is proposed in this paper. Finally, the experimental results show that the accuracy of target detection by GLRT is improved, and the signal-to-noise ratio is also improved under the condition of without-direct-path signals. Moreover, the accuracy of delay detection is improved. [ABSTRACT FROM AUTHOR]

Global navigation satellite systems (GNSS) sensors estimate its position, velocity, and time (PVT) using pseudorange measurements. When there is no interference, the pseudoranges are due to authentic satellites, and the bearings is distinguishable. Whereas, in the presence of any intentional interference source like spoofer, the pseudorange measurements owing to spurious signals and all the bearings from the same direction. These spurious attacks yield either no position or falsified position to the GNSS receiver. This paper proposes to install multiple GNSS receivers on a vehicle (assumed to be cooperative) to detect and mitigate the spoofing attack. While installing multiple GNSS receivers, we assume that each GNSS receiver’s relative position vector (RPV) is assumed to be known to other GNSS receivers. The installed GNSS receivers use the extended Kalman filter (EKF) framework to estimate their PVT. We proposed to calculate the equivalent-measurement and equivalent-measurement covariance of each GNSS receiver in the Cartesian coordinates in the tracklet framework. These tracklets are translated to the vehicle center using RPV to obtain translated-tracklets. The translated tracklet based generalized likelihood ratio test (GLRT) is derived to detect the spoofing attack at a given epoch. In addition to that, these translated-tracklets are processed in a batch least square (LS) framework to obtain the vehicle position. Once the attack is detected at a specific epoch, it quantifies that the position information is false. Moreover, another spoofing test is also formulated using DOA of signals. Once both the tests confirm the spoofing attack, the spoofer localization is performed using pseudo-updated states of GNSS receivers and acquired bearings in the iterative least-squares (ILS) framework. Mitigation of spoofing attack can be achieved either by projecting a null beam in the direction of the spoofer or by launching a counter-attack on the spoofer. The simulation results demonstrate that the proposed algorithm detects spoofing attacks and ensures continuity in the navigation track. As the number of satellite signals increases, the algorithms provide better position root mean square error (PRMSE) for GNSS receivers track, vehicle track, and spoofer localization.

Adopting cognitive radios (CRs) having multiple antennas in blind non-cooperative and cooperative spectrum sensing (CSS) under fading channels has gained attention due to higher detection performances provided by the spatial diversity gain of multi-sensors in different geographical locations and lower complexity, respectively. However, most studies do not consider sensing scenarios of more practical significance: for example, sometimes adopting only uncalibrated antenna arrays and sometimes only correlated signals at antenna arrays of CRs, but almost always, none of these impairments. Therefore, this paper studies these combined impairments on the performances of two blind techniques in centralized CSS with decision fusion (DF) and data fusion for different numbers of CRs and antennas per CR, both under frequency selective fading channels. One is the circular folding cooperative power spectral density split cancellation (CFCPSC), and the other is the generalized likelihood ratio test (GLRT). Extensive numerical results show, for instance, that with sample fusion (SF) and calibrated antennas, GLRT outperforms CFCPSC independently of correlation, numbers of antennas, or CRs. However, uncalibrated antennas severely penalize GLRT while surprisingly benefiting CFCPSC. Correlation is detrimental to GLRT and CFCPSC with SF but may help CFCPSC in DF and GLRT in DF or eigenvalue fusion. Generally, CFCPSC outperforms GLRT. [ABSTRACT FROM AUTHOR]

The detection of multiple interfering persistent scatterers (PSs) using Synthetic Aperture Radar (SAR) tomography is an efficient tool for generating point clouds of urban areas. In this context, detection methods based upon the polarization information of SAR data are effective at increasing the number of PSs and producing high-density point clouds. This paper presents a comparative study on the effects of the polarization design of a radar antenna on further improving the probability of detecting persistent scatterers. For this purpose, we introduce an extension of the existing scattering property-based generalized likelihood ratio test (GLRT) with realistic dependence on the transmitted/received polarizations. The test is based upon polarization basis optimization by synthesizing all possible polarimetric responses of a given scatterer from its measurements on a linear orthonormal basis. Experiments on both simulated and real data show, by means of objective metrics (probability of detection, false alarm rate, and signal-to-noise ratio), that polarization waveform optimization can provide a significant performance gain in the detection of multiple scatterers compared to the existing full-polarization-based detection method. In particular, the increased density of detected PSs at the studied test sites demonstrates the main contribution of the proposed method. [ABSTRACT FROM AUTHOR]

Summary: Spectrum sensing in cognitive radio networks is vital and is used for identifying the user presence or absence in the available spectrum. Energy detection and matched filter detection are the few methods to identify the user presence in the spectrum. There are various authors that proposed their research on spectrum sensing using matched filter detection with fixed threshold and predefined dynamic threshold. In this paper, authors proposed the novel matched filter detection method with dynamic threshold by using generalized likelihood ratio test (GLRT) and Neyman Pearson (NP) observer detection criteria. Due to which the probability of detection (PD) is increased, probability of false alarm (Pfa) and probability of missed detection (Pmd) has been reduced when compare with the existing methods. The results are simulated using MATLab Software and also plotted the receiver operating characteristic (ROC) curve for estimation of the receiver sensitivity. [ABSTRACT FROM AUTHOR]

A method was proposed in this paper for detecting radar targets in sea clutter by solving the composite hypothesis testing for the quasi-optimum detector. The quasi-optimal detector and the sea clutter distribution were assumed to be coherent and based on the K-model. A linear model was proposed based on these assumptions and the generalized likelihood ratio test (GLRT) to compare the detector, outputs after mathematical calculations and approximations. The purpose of this paper was to provide a practical method for the implementation of a detector to reduce the burden of mathematical calculations. Computerized simulations demonstrated the favorable performance of the proposed model. [ABSTRACT FROM AUTHOR]

Intuition suggests that in a large-size sensor network, event signal spreads over a sub-region known as event region. The sub-set of sensors inside the event region is located close to each other in the form of a cluster. On the contrary, the reports from the rest of the sensors that are far away from the event will be less credible and prone to faulty detection. In this work, such fact is exploited to improve the detection performance of the detection schemes at the fusion center by proposing a two-stage detection mechanism. In other words, the proposed two-stage detection scheme exploits the spatial proximity of the event region sensors. Numerical results are provided to assess the detection performance of the proposed scheme that shows that the proposed detection scheme has a superior performance over the traditional detection schemes. [ABSTRACT FROM AUTHOR]

In this paper we consider a zero-truncated form of the hyper-Poisson distribution and investigate some of its crucial properties through deriving its probability generating function, cumulative distribution function, expressions for factorial moments, mean, variance and recurrence relations for probabilities, raw moments and factorial moments. Further, the estimation of the parameters of the distribution is discussed. The distribution has been fitted to certain real life data sets to test its goodness of fit. The likelihood ratio test procedure is adopted for checking the significance of the parameters and a simulation study is performed for assessing the efficiency of the maximum likelihood estimators.

Abstract Relay identification is necessary in many cooperative communication applications such as detecting the presence of malicious relays for communication security, selecting the intended relays for signal forwarding, and tracing a specific relay. However, this identification task becomes extremely challenging for amplify-and-forward (AF) relaying systems since AF relays usually have no capability of adopting traditional identification methods implemented above the physical layer. This paper proposes a physical-layer AF relay identification scheme based on the exploitation of the device-specific in-phase and quadrature-phase imbalance (IQI) feature. Given that IQI estimation is mandatory in most present receivers for compensation, it is cost-effective to make use of these estimation results for fingerprinting AF relays. A generalized likelihood ratio test-based fingerprint differentiation technique is adopted to detect the minor difference between two range-limited IQI fingerprints. Using this differentiation technique, a whitelist-based identification algorithm consisting of fingerprint registration, update, and identification is proposed. Furthermore, the optimal training signals that lead to the maximal detection probability are derived for the typical quadrature amplitude modulation and phase-shift keying modulation schemes. The simulation results validate our derivations and confirm that the proposed method can accurately identify AF relays.

Millimeter-wave (mm-wave) communication has been widely applied in the small cellular networks, and it is not difficult to find that mm-wave plays an important role in improving the capacity and data transmission rate of the 5G network systems. Nevertheless, mm-waves also have some disadvantages, such as large propagation loss and weak penetration blockage. Although the extremely high direction of the mm-wave can compensate for the propagation loss, highly directional transmissions of the mm-wave result in conventional omnidirectional transmission cell discovery schemes may fail in mm-waves; thus, we need to redesign the cell discovery scheme of mm-wave. In addition, the overhead of mm-wave cell discovery is also large. In order to reduce the overhead and provide a reasonable solution for the mm-wave base station (BS) discovery, we propose an analysis framework that utilizes out-of-band (OOB) information assistance. First, we consider the BS discovery problem as a hypothesis test problem. We analyze the statistical properties of the generalized logarithm likelihood ratio detector and study the detection performance of the detector. Through analysis and calculation, we are able to find that the OOB scheme can not only improve the detection performance but also reduce the system overhead. Then, we also analyze the performance of the OOB scheme in the case of multiple UEs. Finally, we use the angle information provided by the OOB information to predesign the mm-wave codebooks to achieve the optimal expected mode. Analysis of the simulation results demonstrates the effectiveness of the OOB information-assisted scheme.

The detection of multiple interfering persistent scatterers (PSs) using Synthetic Aperture Radar (SAR) tomography is an efficient tool for generating point clouds of urban areas. In this context, detection methods based upon the polarization information of SAR data are effective at increasing the number of PSs and producing high-density point clouds. This paper presents a comparative study on the effects of the polarization design of a radar antenna on further improving the probability of detecting persistent scatterers. For this purpose, we introduce an extension of the existing scattering property-based generalized likelihood ratio test (GLRT) with realistic dependence on the transmitted/received polarizations. The test is based upon polarization basis optimization by synthesizing all possible polarimetric responses of a given scatterer from its measurements on a linear orthonormal basis. Experiments on both simulated and real data show, by means of objective metrics (probability of detection, false alarm rate, and signal-to-noise ratio), that polarization waveform optimization can provide a significant performance gain in the detection of multiple scatterers compared to the existing full-polarization-based detection method. In particular, the increased density of detected PSs at the studied test sites demonstrates the main contribution of the proposed method.

In this letter, the distributed target detection in a mixture of unknown communication interference and noise using the orthogonal frequency division multiplexing (OFDM) radar is considered. A closed-form detector is not tractable, instead, an efficient expectation maximization-based generalized likelihood ratio test (GLRT) detector is developed. The ideal GLRT detector with the complete knowledge of communication signal is also derived as a contrast. Besides, an information theoretic waveform design method by maximizing Kullback-Leibler divergence is addressed to improve the detection performance, in which the subcarrier power ratio is used as a constraint. Simulations show the superiority of the addressed detectors and method. [ABSTRACT FROM AUTHOR]

In some radar applications, neither the channel parameters nor the transmitted signal are known a-priori at the radar receiver. As such, both the transmitted signal as well as the channel parameters are random variables. Given the knowledge of distributions of the unknown parameters, we propose a maximum a-posteriori (MAP) ratio test (MAPRT) detector that incorporates this knowledge into its decision making process. This MAP approach is a generalization of past approaches such as the generalized likelihood ratio test (GLRT) detectors, where the estimates of the unknown parameters are obtained using maximum likelihood (ML) estimation. Assuming no knowledge of transmit waveforms, we derive the test statistics for bi-static and multi-static radar systems, when the noise power is both known and unknown. For the case of known noise variance in the bi-static radar system, we derive an exact closed-form expression for the probability of false alarm (PFA) and an approximate expression for the probability of detection (PD). For the multi-static case with known noise power, we derive upper bounds on PFA and PD. Numerical simulations verify the accuracy of the derived expressions and demonstrate that the proposed MAPRT detector performs significantly better than the GLRT-based detectors, especially when the signal-to-noise ratio (SNR) associated with the surveillance channel or sample size assumes small values. [ABSTRACT FROM AUTHOR]

In cognitive radio (CR) networks, eigenvalue-based detectors (EBDs) have attracted much attention due to their good performance of detecting secondary users (SUs). In order to further improve the detection performance of EBDs with short samples, we propose two new detectors: average circulant matrix-based Roy’s largest root test (ACM-RLRT) and average circulant matrix-based generalized likelihood ratio test (ACM-GLRT). In the proposed method, the circulant matrix of samples at each time instant from SUs is calculated, and then, the covariance matrix of the circulant matrix is averaged over a short period of time. The eigenvalues of the achieved average circulant matrix (ACM) are used to build our proposed detectors. Using a circulant matrix can improve the dominant eigenvalue of covariance matrix of signals and also the detection performance of EBDs even with short samples. The probability distribution functions of the detectors undernull hypothesis are analyzed, and the asymptotic expressions for the false-alarm and thresholds of two proposed detectors are derived, respectively. The simulation results verify the effectiveness of the proposed detectors.

Abstract In this paper, a novel statistical approach is presented for time-of-arrival (TOA) estimation based on first path (FP) pulse detection using a sub-Nyquist sampling ultra-wide band (UWB) receiver. The TOA measurement accuracy, which cannot be improved by averaging of the received signal, can be enhanced by the statistical processing of a number of TOA measurements. The TOA statistics are modeled and analyzed for a UWB receiver using threshold crossing detection of a pulse signal with noise. The detection and estimation scheme based on the Generalized Likelihood Ratio Test (GLRT) detector, which captures the full statistical information of the measurement data, is shown to achieve accurate TOA estimation and allows for a trade-off between the threshold level, the noise level, the amplitude and the arrival time of the first path pulse, and the accuracy of the obtained final TOA.

Security of global navigation satellite systems (GNSS) is important since the navigation capability provided by the GNSS is a key enabler for many civilian and military applications. Spoofing attacks threaten the GNSS security and have caught much attention recently. The spatial processing method is one of the most robust GNSS spoofing countermeasures, which detects spoofing signals with a moving antenna or multi-antenna, but it cannot work in a static single-antenna receiver. In this paper, we propose a spoofing countermeasure based on the power measurements of a single rotating antenna, which can be implemented in a static receiver. The method takes advantages of the anisotropy of the antenna's gain pattern to detect spoofing signals. When the antenna is rotating, the power measurements of the spoofing signals coming from the same direction change similarly and the correlation coefficients between them are close to 1, but the power measurements of the authentic signals are uncorrelated. Since it is not easy to evaluate the anti-spoofing performance of the correlation coefficient, another metric named phase difference of power measurements is proposed. Its theoretical performance is derived based on generalized likelihood ratio test and validated with simulations. Actual experiments indicate that both the simulated and meaconing spoofing signals can be distinguished from the authentic ones, and the method can be implemented in a static or low-dynamic conventional receiver, only with an additional low-cost rotary table.

High computational complexity and difficulty in taking an analytical expression of detection statistics are some of the problems encountered when using the statistical resolution limit (SRL) analysis in the generalized likelihood ratio test (GLRT). A Rao test-based method is used in this paper to analyze the 1-D SRL of two closely spaced signals, which can simplify the derivation of detection statistics, reduce the computational complexity, and improve the detection performance in some scenarios. In the proposed approach, two cases are considered based on the interference sources that are either involved or not in the received signals. Moreover, in each case, the detection statistic is derived by assuming the known or unknown variance of the added noise, respectively. For the performance comparison, the GLRT detection statistic is also identified. The relationship between the SRL and the required minimum signal-to-noise ratio/signal-to-interference-plus-noise ratio is also obtained to resolve the two adjacent signal sources. Then, the detection performance and computational complexity are evaluated. The simulation results are used to verify the effectiveness and superiority of the Rao test.

• New receivers for time synchronization of MIMO systems in the presence of interference are proposed. • These receivers are compared with those of the literature from a performance and a complexity point of view. • The optimization of the number of transmitted antennas is investigated for deterministic and random channels. • Receivers of the literature without interference are compared in a unified framework with the GLRT1 receiver. The time synchronization of (M × N) MIMO systems has been studied this last fifteen years, for both single-carrier (SC) and multi-carriers links. Without any interference, most of the available receivers assume orthogonal sequences. With interference, the current most powerful receiver is a generalized likelihood ratio test (GLRT) receiver, assuming unknown, stationary, circular, temporally white and spatially colored Gaussian noise. However, this receiver is more complex than its non-GLRT counterparts, which, unfortunately, do not perform as well in most cases. In this context, the purpose of this paper is to get new insights into the time synchronization of SC MIMO links, both without and with interference, in order to overcome the limitations of the available receivers. In the absence of interference, the MIMO GLRT receiver is computed and compared to the existing ones in a unified framework, enlightening its better performance. In the presence of interference, as the complexity is an important issue in practice, several ways to decrease the complexity of the available GLRT receiver while keeping its performance are proposed, enlightening the great practical interest of the proposed schemes. Finally, the optimization of the number of transmit antennas is investigated, enlightening the existence of an optimal value of M depending on the channel matrix. [ABSTRACT FROM AUTHOR]

Abstract In a wireless sensor network (WSN), nodes usually send their processed data (their decisions, in the extreme case) to a fusion center (FC) which takes the final decision by applying an appropriate fusion rule on received decisions. The decisions of nodes are generally correlated. However, complicated fusion rules should be implemented if the correlation is considered, even in the simplest case of a two-sensor network. In this paper, a decision fusion scheme based on the Plackett copula is proposed in which the correlation among nodes is considered. The simulation results show that the proposed Plackett fusion (PF) scheme improves appropriately the detection performance in a serial network. [ABSTRACT FROM AUTHOR]

Abstract A gas kick may occur when drilling with narrow pressure margin, which can lead to significant non-productive time. This paper proposes an early gas kick detection method applicable to water-based mud (WBM), which integrates a transient pressure and temperature coupling model into an unscented Kalman filter (UKF) algorithm. Three pressure factors and a flow rate factor are estimated with the UKF algorithm using updated measurement data in a detection estimator, and a generalized likelihood ratio test (GLRT) is employed to automatically detect changes in the pressure factors and flow rate factor for prediction of the gas kick. Simulated results show that the estimated pressures and outlet flow rate trace synthetic measurements very well with continuous inversion of the pressure factors and flow rate factor. All of the estimated pressure factors and flow rate factor fluctuate to a little extent around a base value under normal drilling condition, while the pressure factor in annulus and flow rate factor both deviate from the base value when the gas kick occurs. Performances of the proposed method, pit gain monitoring method and delta flow monitoring method are contrasted. The kick detection times are gradually reduced for all the methods with the increase of gas kick rate. Moreover, the proposed method herein shows a much better performance than the pit gain and delta flow monitoring methods, especially at small gas kick rate. Highlights • A new early gas kick detection method is proposed. • Pressure factors and flow rate factor are estimated by unscented Kalman filter. • Temperature effect is considered in the detection method. • Superior performance of the gas kick detection method has been proved. [ABSTRACT FROM AUTHOR]

Spectrum sensing in next-generation wireless radio networks is considered a key technology to overcome the problem of spectrum scarcity. Unfortunately, many approaches to spectrum sensing do not work well in low signal-to-noise ratio (SNR) environments. This paper proposes and analyzes a new algorithm named kernelized generalized likelihood ratio test (KGLRT) for spectrum sensing in cognitive radio systems to overcome this problem. Effectively, KGLRT uses a nonlinear kernel to map input data onto a high-dimensional feature space; then, the widely accepted (linear) generalized likelihood ratio test is used for hypothesis testing. This new algorithm gives a gain of 4 dB in SNR over its linear counterpart. A theoretical analysis for this algorithm is given for the first time and is shown analogous to algorithms used in image signal processing. The detection metrics are found to be concentrated random variables; furthermore, the probability distributions of the detection metrics are proved to follow the F -distributions, which agree with the results obtained using the concentration inequality. The analytical thresholds are derived for target false-alarm probabilities. The thresholds are independent of noise power; thus, the proposed algorithm can overcome noise uncertainty issues at very low SNR levels. Simulations validate the theoretical results. [ABSTRACT FROM AUTHOR]

Passive bistatic radars (PBRs) need to use long integration times to achieve ranges of interest for air traffic surveillance applications. However, with the increase of integration time, classical target detection algorithms used in passive radars encounter some limitations in detecting accelerating targets. This is mainly due to a simplified target’s motion model used for detector design. In such cases, the mismatch between the target’s simplified motion model and that of the actual target motion limits any improvement in signal-to-noise ratio. To cure this especially in the presence of the accelerating targets, we obtain the received signal model and formulate target detection problem as an M-ary hypothesis testing problem. Then, we apply generalized likelihood ratio principle to derive a new 3-D sequential detection algorithm. In the proposed detector, range-Doppler-acceleration coordinates of detectable targets are estimated as compared to the classical target detection algorithms with range-Doppler estimations. To effectively implement the proposed detector over the desired range-Doppler-acceleration map, we use a modified version of chirp fast Fourier transform. We also obtain a closed-form expression for false alarm probability to adjust the detection threshold. Simulation results are provided to illustrate the superiority of the proposed 3-D-detection algorithm over state-of-the-art classical target detection algorithm given in the context of FM-based PBR systems. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of testing whether, after linear transformations and possible dimensionality reductions, a random matrix of interest Θ deviates significantly from some matrix model θ 0 , when Θ is observed in additive independent Gaussian noise with known covariance matrix. In contrast to standard likelihood theory, the probability distribution of Θ is assumed to be unknown. This problem generalizes the Random Distortion Testing (RDT) problem addressed in a former paper. Although the notions of size and power can be extended so as to deal with this generalized problem, no Uniformly Most Powerful (UMP) test exists for it. We can however exhibit a relevant subclass of tests and prove the existence of an optimal test within this class, that is, a test with specified size and maximal constant conditional power. As a consequence, this test is also UMP among invariant tests. The method fits within a wide range of signal processing scenarios. It is here specifically applied to sequential detection, subspace detection and random distortion testing after space-time compressive sensing. It is also used to extend the GLRT optimality properties for testing a waveform amplitude in noise. [ABSTRACT FROM AUTHOR]

Consider an inhomogeneous Poisson processXon [0,T] whose unk-nown intensity function “switches” from a lower functiong to an upper functionh at some unknown point ϑ that has to be identified. We consider two known continuous functionsgandhsuch thatg (t) ⩽g(t)

This paper deals with the problem of testing the equality of $M$ covariance matrices. We first identify a suitable group of transformations leaving the problem invariant and obtain the corresponding maximal invariant statistic. Then, the Generalized Likelihood Ratio Test (GLRT) is recalled and explicit expressions for Rao, Wald, Gradient, and Durbin tests are provided. Also, equivalences among them and with other well-known tests proposed in open literature (mostly for the real-valued case) are analyzed and compared. Finally, the application of the proposed framework to the relevant signal processing application of multipass Coherent Change Detection (CCD) in polarimetric synthetic aperture radar is demonstrated both on simulated and on live data. [ABSTRACT FROM PUBLISHER]

With exponential rise in the internet applications and wireless communications, higher and efficient data transfer rates are required. Hence proper and effective spectrum is the need of the hour, As spectrum demand increases there are limited number of bands available to send and receive the data. Optimizing the use of these bands efficiently is one of the tedious tasks. Various techniques are used to send the data at same time, but for that we have to know which bands are free before sending the data. For this purpose various spectrum sensing approaches came with variety of solutions. In this paper the sensing problem is tackled with the use of hybrid spectrum sensing method, This new networking paradox uses the Centralized concept of spectrum sensing and creates one of the most trusted spectrums sensing mechanism. This proposed technique is simulated using MATLAB software. This paper also provides comparative study of various spectrum sensing methodologies. [ABSTRACT FROM AUTHOR]

In this paper, a novel statistical approach is presented for time-of-arrival (TOA) estimation based on first path (FP) pulse detection using a sub-Nyquist sampling ultra-wide band (UWB) receiver. The TOA measurement accuracy, which cannot be improved by averaging of the received signal, can be enhanced by the statistical processing of a number of TOA measurements. The TOA statistics are modeled and analyzed for a UWB receiver using threshold crossing detection of a pulse signal with noise. The detection and estimation scheme based on the Generalized Likelihood Ratio Test (GLRT) detector, which captures the full statistical information of the measurement data, is shown to achieve accurate TOA estimation and allows for a trade-off between the threshold level, the noise level, the amplitude and the arrival time of the first path pulse, and the accuracy of the obtained final TOA. [ABSTRACT FROM AUTHOR]

Due to 64- \mu \texts line fly-back time of the analog television (ATV) signal producing a range ambiguity at ranges corresponding to the multiples of 19.2 km, it seems that we cannot determine the targets’ ranges in ATV-based passive bistatic radars (PBRs). Based on this standpoint, all discovered works on a target-detection problem in an ATV-based PBR have focused on the Doppler information in echoes of the television video carrier signal not range information. To cope with the severe range ambiguity of the ATV signal and make it possible to jointly estimate targets’ delays and Doppler shifts, we propose a new detector performing target detection in a layered manner. In each layer, one target is detected. Detection in each layer is done using a generalized likelihood ratio detector. Interference due to the detected target in the first layer is estimated and subtracted from the received signal. From the first layer’s interference canceled signal, another target (if exist) is detected in the second layer. The interference due to the second layer’s detected target is estimated and subtracted. These detection and interference cancellation steps are carried out in each layer, until all the targets are detected. The proposed detector is also implemented based on the chirp z-transform to efficiently obtain targets’ Doppler shifts over a limited range of Doppler frequencies. To obtain an effective cancellation of the strong interfering targets as well as off-grid targets, a robust version of resulting algorithm is also presented. Extensive simulation results are provided to demonstrate the high performance of the resulting target-detection algorithms. [ABSTRACT FROM PUBLISHER]

In this paper we tackle distributed detection of a non-cooperative target with a Wireless Sensor Network (WSN). When the target is present, sensors observe an unknown random signal with amplitude attenuation depending on the distance between the sensor and the target (unknown) positions, embedded in white Gaussian noise. The Fusion Center (FC) receives sensors decisions through error-prone Binary Symmetric Channels (BSCs) and is in charge of performing a (potentially) more-accurate global decision. The resulting problem is a one-sided testing with nuisance parameters present only under the target-present hypothesis. We first focus on fusion rules based on Generalized Likelihood Ratio Test (GLRT), Bayesian and hybrid approaches. Then, aimed at reducing the computational complexity, we develop fusion rules based on generalizations of the well-known Locally-Optimum Detection (LOD) framework. Finally, all the proposed rules are compared in terms of performance and complexity. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new approach for the detection of OFDMA and other wideband signals in the context of centralized cooperative spectrum sensing for cognitive radio (CR) applications. The approach is based on the eigenvalues of the received signal covariance matrix whose samples are in the frequency domain. Soft combining of the eigenvalues at the fusion center is the main novelty. This combining strategy is applied to variants of four test statistics for binary hypothesis test, namely: the eigenvalue-based generalized likelihood ratio test (GLRT), the maximum-minimum eigenvalue detection (MMED), the maximum eigenvalue detection (MED) and the energy detection (ED). It is shown that the eigenvalue fusion can outperform schemes based on decision fusion and sample fusion. A tradeoff is also established between complexity and volume of data sent to the fusion center in all combining strategies.

In disaster rescue, trapped survivors with regular respiration can be located, by detecting regular respiratory signals (RRSs) acquired with life‐detection radar systems. RRSs are often weak in these scenarios, due to the attenuation of the electromagnetic waves that propagate through debris. Thus, detecting RRSs under low signal‐to‐noise ratio is a key challenge in this application. In this study, RRS detection in additive white Gaussian noise was investigated from a statistical signal processing viewpoint, and a modified generalised‐likelihood ratio test (GLRT) was derived. With proper parameter settings, the modified GLRT (MG) could achieve a notable detection gain over the periodogram test and the harmogram test, two classical periodic signal detectors. Thus, the proposed MG could be used to improve the detection performance of the life‐detection radar systems used in disaster rescue applications. [ABSTRACT FROM AUTHOR]

This paper investigates the asymptotic optimality of the Constant False Alarm Rate (CFAR) tests obtained using the Minimal Invariant Group (MIG) reduction. We show that the CFAR tests obtained after MIG reduction using the Wald test is a Separating Function Estimation Test (SFET) and that the Generalized Likelihood Ratio Test (GLRT) and the Rao test are asymptotically SFET using Maximum Likelihood Estimation (MLE) under some mild conditions. Thus, they are asymptotically optimal. In order to find an improved test and motivated by the invariance property of the MLE of induced maximal invariant, we maximize the asymptotic Probability of Detection of the SFET using the MLE after reduction. We propose a systematic method allowing to derive the asymptotically optimal Separating Function (AOSF). This AOSF is obtained as the Euclidean distance of the transformed parameters under two hypotheses such that the gradient of the transformed parameters is the Cholesky decomposition of the Fisher Information Matrix (FIM), i.e., the FIM is transformed into an identity matrix. Interestingly, the AOSF Estimation Test (AOSFET) using MLE simplifies to the Wald-CFAR wherever the FIM does not depend on the unknown parameters. The simulation results show that the proposed AOSFET usually outperforms the GLRT, Wald test and Rao test. [ABSTRACT FROM AUTHOR]

A central limit theorem for bilinear forms of the type a ∗ C ˆ N ( ρ ) − 1 b , where a , b ∈ C N are unit norm deterministic vectors and C ˆ N ( ρ ) a robust-shrinkage estimator of scatter parametrized by ρ and built upon n independent elliptical vector observations, is presented. The fluctuations of a ∗ C ˆ N ( ρ ) − 1 b are found to be of order N − 1 2 and to be the same as those of a ∗ S ˆ N ( ρ ) − 1 b for S ˆ N ( ρ ) a matrix of a theoretical tractable form. This result is exploited in a classical signal detection problem to provide an improved detector which is both robust to elliptical data observations (e.g., impulsive noise) and optimized across the shrinkage parameter ρ . [ABSTRACT FROM AUTHOR]