Quadratic FM Signal Detection and Parameter Estimation Using Coherently Integrated Trilinear Autocorrelation Function This paper proposes an integrated timefrequency rate distribution (TFRD) technique, i.e., coherently integrated trilinear autocorrelation function (CITAF), for the detection and parameter estimation of quadratic frequency modulated (QFM) signal embedded in white Gaussian noise. The basic idea is to design a trilinear autocorrelation function (TAF) enabling a coherent integration of the signal energy in both of the time and lagtime domains. Theoretical analysis and numerical simulations show that this new method has a lower signal to noise ratio (SNR) threshold and higher estimation accuracy in comparison with several stateoftheart algorithms. Furthermore, the coherently integrated nonuniform TAF (CINTAF) is briefly introduced.

Spectrum Activity Estimation by Partitionblind Block Partitioned Tensor Decomposition We consider a scenario where signals that can be modeled as finite sums of linearly modulated signals that are nonorthogonal in both time and frequency are observed by a sensor. Under this model, when an antidiagonal slice of the trispectum of the sum of these signals is computed for multiple segments in time and stacked into a frontal symmetric block partitioned tensor (FSBPT) whose factor matrices characterize the received power spectrum and transmission activities of the signals. We propose a new alternating soft thresholding decomposition strategy that is tailored to FSBPTs and, unlike existing BPT decomposition algorithms, simultaneously estimates the factor partitions which is necessary since in our signal model the partitioning is not known a priori. We demonstrate the algorithmxe2x80x99s performance via Monte Carlo simulations. We find that our proposed nonblind algorithm is able to estimate the tensor partitions and moderately accurately estimate the factor matrices under a range of factor matrix column collinearity values.