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This letter incorporates the adaptive kernel Kalman filter (AKKF) into the belief propagation (BP) algorithm for Multi-target tracking (MTT) in single-sensor systems. The algorithm is capable of tracking an unknown and time-varying number of targets, in the presence of false alarms, clutter and measurement-to-target association uncertainty. Experiment results reveal that the proposed method has a favourable tracking performance using the generalized optimal sub-patten assignment (GOSAP) metrics at substantially less computation cost than the particle filter (PF) based Multi-target tracking (MTT) BP algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a generalized signal model is presented to accommodate both narrowband and wideband signals in a multi-input multi-output (MIMO) sensor system scenarios. The derived model is then used to define a MIMO ambiguity function based on the Kullback–Leibler divergence. Moreover, the proposed formulation is parametrized using the signal and channel correlation matrices to account for different waveform and sensor placement designs, thereby allowing a flexible modeling approach. A comparison between the proposed definition and the more conventional approach of summing the squared matched filter outputs is presented for different sensors and waveforms configurations. [ABSTRACT FROM AUTHOR]

This paper addresses the extension of the factorization of a Hermitian matrix by an eigenvalue decomposition (EVD) to the case of a parahermitian matrix that is analytic at least on an annulus containing the unit circle. Such parahermitian matrices contain polynomials or rational functions in the complex variable $z$ and arise, e.g., as cross spectral density matrices in broadband array problems. Specifically, conditions for the existence and uniqueness of eigenvalues and eigenvectors of a parahermitian matrix EVD are given, such that these can be represented by a power or Laurent series that is absolutely convergent, at least on the unit circle, permitting a direct realization in the time domain. Based on an analysis of the unit circle, we prove that eigenvalues exist as unique and convergent but likely infinite-length Laurent series. The eigenvectors can have an arbitrary phase response and are shown to exist as convergent Laurent series if eigenvalues are selected as analytic functions on the unit circle, and if the phase response is selected such that the eigenvectors are Hölder continuous with $\alpha >\frac{1}{2}$ on the unit circle. In the case of a discontinuous phase response or if spectral majorisation is enforced for intersecting eigenvalues, an absolutely convergent Laurent series solution for the eigenvectors of a parahermitian EVD does not exist. We provide some examples, comment on the approximation of a parahermitian matrix EVD by Laurent polynomial factors, and compare our findings to the solutions provided by polynomial matrix EVD algorithms. [ABSTRACT FROM AUTHOR]

Presents corrections to the paper, "On the existence and uniqueness of the eigenvalue decomposition of a paraher mitian matrix," (Weiss, S. et al), IEEE Trans. Signal Process., vol. 66, no. 10, pp. 2659–2672, May 2018. [ABSTRACT FROM AUTHOR]

A new fast recursive least squares (RLS) algorithm is introduced. By making use of RLS interpolation as well as prediction, the algorithm generates the transversal filter weights without suffering the poor numerical attributes of the FTP algorithm. The Kalman gain vector is generated at each time step in terms of interpolation residuals. The interpolation residuals are calculated in an order recursive manner. For an Nth-order problem, the procedure requires O(N log[sub2] N) operations per iteration. This is achieved via a divide-and-conquer approach. Computer simulations suggest that the new algorithm is numerically robust, running successfully for many millions of iterations. [ABSTRACT FROM AUTHOR]

Presents a study which evaluated numerical analysis techniques for signal processing algorithms. Theoretical background; Numerical representation; Results and discussion.

Presents a study which discussed the minimum variance distortionless response beamforming algorithm. Application of the algorithm to least-square problems; Conclusion.