Efficient Centralized Cooperative Spectrum Sensing Techniques for Cognitive Networks

Wireless Communication is a system for communicating information from one point to other,without utilizing any connections like wire,cable,or other physical medium.Cognitive Radio(CR)based systems and networks are a revolutionary new perception in wireless communications.Spectrum sensing is a vital task of CR to avert destructive intrusion with licensed primary or main users and discover the accessible spectrum for the efficient utilization of the spectrum.Centralized Cooperative Spectrum Sensing(CSS)is a kind of spectrum sensing.Most of the test metrics designed till now for sensing the spectrum is produced by using the Sample Covariance Matrix(SCM)of the received signal.Some of the methods that use the SCM for the process of detection are Pietra-Ricci Index Detector(PRIDe),Hadamard Ratio(HR)detector,Gini Index Detector(GID),etc.This paper presents the simulation and comparative perfor-mance analysis of PRIDe with various other detectors like GID,HR,Arithmetic to Geometric Mean(AGM),Volume-based Detector number 1(VD1),Maximum-to-Minimum Eigenvalue Detection(MMED),and Generalized Likelihood Ratio Test(GLRT)using the MATLAB software.The PRIDe provides better performance in the presence of variations in the power of the signal and the noise power with less computational complexity.

Hypothesis testing analysis and unknown parameter estimation of GPS signal detection

Hypothesis testing analysis and unknown parameter estimation of both the intermediate frequency(IF) and baseband GPS signal detection are given by using the generalized likelihood ratio test(GLRT) approach,applying the model of GPS signal in white Gaussian noise,It is proved that the test statistic follows central or noncentral F distribution,It is also pointed out that the test statistic is nearly identical to central or noncentral chi-squared distribution because the processing samples are large enough to be considered as infinite in GPS acquisition problem.It is also proved that the probability of false alarm,the probability of detection and the threshold are affected largely when the hypothesis testing refers to the full pseudorandom noise(PRN) code phase and Doppler frequency search space cells instead of each individual cell.The performance of the test statistic is also given with combining the noncoherent integration.

Distribution-based CFAR detectors in SAR images

As traditional two-parameter constant false alarm rate （CFAR） target detec-tion algorithms in SAR images ignore target distribution, their performances are not the best or near best. As the resolution of SAR images increases, small targets present more pixels in SAR images. So the target distribution is of much significance. Distribution-based CFAR detection algorithm is presented. We unite the pixels around the test cell, and estimate the distribution of test cell by them. Generalized Likelihood Ratio Test （GLRT） is used to deduce the detectors. The performance of the distribution-based CFAR （DBCFAR） detectors is analyzed theoretically. False alarms of DBCFAR detection are fewer than those of CFAR at the same detection rate. Finally experiments are done and the results show the performance of DBCFAR is out of conventional CFAR. False alarms of DBCFAR detection are concentrated while those of CFAR detection are dispersive.

The Latest Progress in Varying-coefficient Models

Varying-coefficient models are a useful extension of classical linear model. They are widely applied to economics, biomedicine, epidemiology, and so on. There are extensive studies on them in the latest three decade years. In this paper, many of models related to varying-coefficient models are gathered up. All kinds of the estimation procedures and theory of hypothesis test on the varying-coefficients model are summarized. Prom my opinion, some aspects waiting to study are proposed.

Adaptive detector design of MIMO radar with unknown covariance matrix

The problem of detecting signal with multiple input mul-tiple output （MIMO） radar in correlated Gaussian clutter dominated scenario with unknown covariance matrix is dealt with. The gen-eral MIMO model, with widely separated sub-arrays and co-located antennas at each sub-array, is adopted. Firstly, the generalized likelihood ratio test （GLRT） with known covariance matrix is ob-tained, and then the Rao and Wald detectors are devised, which have proved that the Rao and Wald test coincide with GLRT detec-tor. To make the detectors fully adaptive, the secondary data with signal-free will be collected to estimate the covariance. The per-formance of the proposed detector is analyzed, however, it is just ancillary. A thorough performance assessment by several numer-ical examples is also given, which has considered the sense with co-located antennas configure of transmitters and receivers array. The results show that the performance the proposed adaptive de-tector is better than LJ-GLRT, and the loss can be acceptable in comparison to their non-adaptive counterparts.

Adaptive detection in the presence of signal mismatch

The problem of adaptive detection in the situation of signal mismatch is considered; that is, the actual signal steering vector is not aligned with the nominal one. Two novel tunable detectors are proposed. They can control the degree to which the mismatched signals are rejected. Remarkably, it is found that they both cover existing famous detectors as their special cases. More importantly, they possess the constant false alarm rate（CFAR）property and achieve enhanced mismatched signal rejection or improved robustness than their natural competitors. Besides, they can provide slightly better matched signals detection performance than the existing detectors.

Improved AR-Based GLRT Detector for Range-Spread Targets in Compound-Gaussian Clutter without Secondary Data

The problem of adaptive radar detection in compound-Gaussian clutter without secondary data is considered in this paper.In most practical applications,the number of training data is limited.To overcome the lack of training data,an autoregressive（AR）-process-based covariance matrix estimator is proposed.Then,with the estimated covariance matrix the one-step generalized likelihood ratio test（GLRT） detector is designed without training data.Finally,detection performance of our proposed detector is assessed.

Statistical inference for the nonparametric and semiparametric hidden Markov model via the composite likelihood approach

In this paper, we propose a new estimation method for a nonparametric hidden Markov model(HMM), in which both the emission model and the transition matrix are nonparametric, and a semiparametric HMM, in which the transition matrix is parametric while emission models are nonparametric. The estimation is based on a novel composite likelihood method, where the pairs of consecutive observations are treated as independent bivariate random variables. Therefore, the model is transformed into a mixture model, and a modified expectation-maximization(EM) algorithm is developed to compute the maximum composite likelihood.We systematically study asymptotic properties for both the nonparametric HMM and the semiparametric HMM. We also propose a generalized likelihood ratio test to choose between the nonparametric HMM and the semiparametric HMM. We derive the asymptotic distribution and prove the Wilk’s phenomenon of the proposed test statistics. Simulation studies and an application in volatility clustering analysis of the volatility index in the Chicago Board Options Exchange(CBOE) are conducted to demonstrate the effectiveness of the proposed methods.

Tests for nonparametric parts on partially linear single index models

Tests for nonparametric parts on partially linear single index models are considered in this paper. Based on the estimates obtained by the local linear method, the generalized likelihood ratio tests for the models are established. Under the null hypotheses the normalized tests follow asymptotically the χ2-distribution with the scale constants and the degrees of freedom being independent of the nuisance parameters, which is called the Wilks phenomenon. A simulated example is used to evaluate the performances of the testing procedures empirically.

A novel target detection approach based on adaptive radar waveform design

To resolve problems of complicated clutter, fast-varying scenes, and low signal-clutterratio （SCR） in application of target detection on sea for space-based radar （SBR）, a target detection approach based on adaptive waveform design is proposed in this paper. Firstly, complicated sea clutter is modeled as compound Gaussian process, and a target is modeled as some scatterers with Gaussian reflectivity. Secondly, every dwell duration of radar is divided into several sub-dwells. Regular linear frequency modulated pulses are transmitted at Sub-dwell 1, and the received signal at this sub-dwell is used to estimate clutter covariance matrices and pre-detection. Estimated matrices are updated at every following sub-dwell by multiple particle filtering to cope with fast-varying clutter scenes of SBR. Furthermore, waveform of every following sub-dwell is designed adaptively according to mean square optimization technique. Finally, principal component analysis and generalized likelihood ratio test is used for mitigation of colored interference and property of constant false alarm rate, respectively. Simulation results show that, considering configuration of SBR and condition of complicated clutter, 9 dB is reduced for SCR which reliable detection requires by this target detection approach. Therefore, the work in this paper can markedly improve radar detection performance for weak targets.

Polarimetric synthetic aperture radar change detection for specific land cover types

This paper presents a supervised polarimetric synthetic aperture radar(PolSAR)change detection method applied to specific land cover types.For each pixel of a PolSAR image,its target scattering vector can be modeled as having a complex multivariate normal distribution.Based on this assumption,the joint distribution of two corresponding vectors in a pair of PolSAR images is derived.Then,a generalized likelihood ratio test statistic for the equality of two likelihood functions of such joint distribution is considered and a maximum likelihood distance measure for specific land cover types is presented.Subsequently,the Kittler and Illingworth minimum error threshold segmenta-tion method is applied to extract the specific changed areas.Experiments on two repeat-pass Radarsat-2 fully polarimetric images of Suzhou,China,demonstrate that the proposed change detection method gives a good performance in determining the specific changed areas in PolSAR images,especially the areas that have changed to water.

GLRT Detectors for Aircraft Wake Vortices in Clear Air

In this article, radar echoes of aircraft wake vortices are modeled as weighted sums of the frequency components of the echoes with a special covariance matrix for the weighted coefficients. With a proposed detection scheme, two generalized likelihood ratio test (GLRT) detectors are derived respectively for aircraft wake vortices with time-varying and time-invariant Doppler spectra. Then the analytical expressions for detection and false alarm probabilities of the detectors are derived and three factors are investigated which mainly influence the detection performance, i.e., the Doppler extension and uncertainty of the aircraft wake vortex, and the number of the detection cells. The results indicate that, the signal-to-noise ratio (SNR) loss induced by Doppler extension is generally several decibels. The SNR loss due to Doppler uncertainty is approximately proportional to the logarithm of the number of spectrum lines in the uncertain Doppler spectrum intervals. For a large number of detection cells, the SNR gain is approximately proportional to the square root of the number of the detection cells.

Non-coherent space-time code based on full diversity space-time block coding

A non-unitary non-coherent space-time code which is capable of achieving full algebraic diversity is proposed based on full diversity space-time block coding, The error performance is optimized by transforming the non-unitary space-time code into unitary space-time code, By exploiting the desired structure of the proposed code, a grouped generalized likelihood ratio test decoding algorithm is presented to overcome the high complexity of the optimal algorithm, Simulation results show that the proposed code possesses high spectrum efficiency in contrast to the unitary space-time code despite slight loss in the SNR, and besides, the proposed grouped decoding algorithm provides good tradeoff between performance and complexity,

A novel auto-reference ultra-wideband receiver scheme

A new auto-reference(AR)transmit-receive scheme and a corresponding group decision algorithm on the generalized likelihood ratio testing(GLRT)principle is proposed in this paper to overcome the drawbacks of the power inefficiency and the high noise vulnerability in transmitted-reference(TR)ultra-wideband(UWB)systems.A partly overlapped detection window structure is introduced in which the decided data frames are regarded as the reference signal so that energy and time resources in the reference frame are saved and full-rate data transmission is achieved.Differential coding was utilized to solve the error propagation problem introduced by the overlapped detection windows.The group decision algorithm on the GLRT principle was utilized to estimate the correlation template through all data frames in detection windows and could offer better noise suppression.Simulation results reveal that the AR scheme with its decision algorithm outperforms the conventional and other modified TR schemes in generalized signal-to-noise ratio(SNR).