A novel method for aliasing detection and resolving in the estimation of polynomialphase signal (PPS) parameters is presented. Aliasing is detected using two high-order ambiguity functions (HAFs) of a uniformly sampled PPS embedded in noise. If aliasing occurred, we propose a way of recovering the true parameters from their aliased positions. To that end, a closed-form expression for the true p...

The aim of this paper is to explore the binary sequences with high discrimination using Particle Swarm Optimization with Cauchy Mutation (PSOCM) technique. The cross-ambiguity function method is used to detect the stationary and moving targets in various target detection scenarios. The contour plots of the cross-ambiguity function are computed as a function of delay and Doppler frequency shift....

Abstract AD-MUSIC is introduced to near-field to investigate the bearing and range estimation for the mixed source localization of sinusoidal signal and LFM signal. Firstly, a near-field two-dimensional MUSIC method based on ambiguity function distribution is put forward. Further, a novel Root-MUSIC method which constructs two ambiguity function distribution matrices is proposed for near-field ...

—This paper proposes a novel algorithm of parameter dynamic estimation in near-field Bistatic Multiple-Input Multiple-Output (MIMO) Radar. When a target lies in the nearfield region, the near-field wavefront is spherical, and the plane waves assumption is no valid. The existing methods and signal model based on the far-field assumption are not applicable to this situation. In this paper, we pr...

In many applications, (for example: air traffic control; robotics; ocean surveillance and medical diagnostics) one needs to process and analyse large volumes of data. The multisensor data association problem is concerned with determining which measurements from one or more sensors actually relate to the same object. This problem can be formulated as a multidimensional assignment problem. In the...

Typically with expert systems, the user is asked to assess or categorize evidence (E), based on data (D). The system is then responsible for matching that evidence (E) to the appropriate hypothesis (H). Throughout, the user provides the characterization of data as evidence for the system. Unfortunately, different users may develop different categorizations of data. Based on different points ofv...

Cognitive computing systems require human labeled data for evaluation, and often for training. The standard practice used in gathering this data minimizes disagreement between annotators, and we have found this results in data that fails to account for the ambiguity inherent in language. We have proposed the CrowdTruth method for collecting ground truth through crowdsourcing, that reconsiders t...

Data from previous neuroimaging studies exploring neural activity associated with uncertainty suggest varying levels of activation associated with changing degrees of uncertainty in neural regions that mediate choice behavior. The present study used a novel task that parametrically controlled the amount of information hidden from the subject; levels of uncertainty ranged from full ambiguity (no...

The ?-MEU model and the smooth ambiguity are two popular models in decision making under ambiguity. However, axiomatic foundations of these not completely understood. We provide a symmetric setting with product state space S?. This allows marginals over S to be linked behaviorally (limiting frequency) events. Bets on such events shown reveal i.i.d. measures that relevant for maker's preferences...