- Read more about Optimal Measurement Budget Allocation for Particle Filtering
- Log in to post comments

Particle filtering is a powerful tool for target tracking. When the budget for observations is restricted, it is necessary to reduce the measurements to a limited amount of samples carefully selected. A discrete stochastic nonlinear dynamical system is studied over a finite time horizon. The problem of selecting the optimal measurement times for particle filtering is formalized as a combinatorial optimization problem. We propose an approximated solution based on the nesting of a genetic algorithm, a Monte Carlo algorithm and a particle filter.

- Categories:

Clock synchronization and ranging over a wireless network with low communication overhead is a challenging goal with tremendous impact. In this paper, we study the use of time-to-digital converters in wireless sensors, which provides clock synchronization and ranging at negligible communication overhead through a sawtooth signal model for round trip times between two nodes.

- Categories:

- Read more about Statistical Signal Processing Approach For Rain Estimation Based on Measurements From Network Management Systems
- Log in to post comments

In this talk we present statistical signal processing methodologies on a real-world application of using Commercial Microwave Links (CMLs) as opportunistic sensors for rain monitoring. We formulate an appropriate parameter estimation problem, taking advantage on the empirically evaluated statistics of the rain, and present a new methodology for rain estimation given only the quantized minimum and maximum radio signal level measurements, which are being logged regularly by the network management systems.

- Categories:

- Read more about An Iterative Time Domain Denoising Method
- Log in to post comments

This paper focuses on the classical additive noise signal restoration problem. The proposed time domain denoising method iteratively removes outliers. The proposed denoising filter incorporates a threshold operation to determine which sample values are outliers. This method is compared with wavelet soft/hard thresholding and empirical mode decomposition interval thresholding. The proposed method is shown to be a promising method to denoise signals where a frequency decomposition may not be a robust representation of the noise free signal.

- Categories:

- Read more about Solving Complex Quadratic Equations with Full-rank Random Gaussian Matrices
- Log in to post comments

We tackle the problem of recovering a complex signal $\mathbf{x}\in\mathbb{C}^n$ from quadratic measurements of the form $y_i=\mathbf{x}^*\mathbf{A}_i\mathbf{x}$, where $\{\mathbf{A}_i\}_{i=1}^m$ is a set of complex iid standard Gaussian matrices. This non-convex problem is related to the well understood phase retrieval problem where $\mathbf{A}_i$ is a rank-1 positive semidefinite matrix.

- Categories:

Sensor selection refers to the problem of intelligently selecting a small subset of a collection of available sensors to reduce the sensing cost while preserving signal acquisition performance. The majority of sensor selection algorithms find the subset of sensors that best recovers an arbitrary signal from a number of linear measurements that is larger than the dimension of the signal.

- Categories:

- Read more about IMPROVING MULTIKERNEL ADAPTIVE FILTERING WITH SELECTIVE BIAS
- Log in to post comments

In this paper, we propose a scheme to simplify the selection of kernel adaptive filters in a multikernel structure.

By multiplying the output of each kernel filter by an adaptive biasing factor between zero and one, the degrading effects of poorly adjusted kernel filters can be minimized, increasing the robustness of the multikernel scheme. This approach is able to deal with the lack of the necessary statistical information for an optimal adjustment of the filter and its structure.

- Categories:

- Read more about Assessing cross-dependencies using bivariate multifractal analysis
- Log in to post comments

Multifractal analysis, notably with its recent wavelet-leader based formulation, has nowadays become a reference tool to characterize scale-free temporal dynamics in time series. It proved successful in numerous applications very diverse in nature. However, such successes remained restricted to univariate analysis while many recent applications call for the joint analysis of several components. Surprisingly, multivariate multifractal analysis remained mostly overlooked.

- Categories:

- Read more about A Theory of Generalized Proximity for ADMM
- Log in to post comments

The alternating direction method of multipliers has become in recent years the most widely used proximal method for signal processing. In this paper, we lay the groundwork for a new notion of proximity and use it to illustrate that the method (ADMM) is actually somewhat of a maladroit rearrangement of a new, more practical procedure that generalizes the Douglas-Rachford algorithm. Compared to ADMM, the algorithm that we propose enjoys not only a more sensible form, but also a more general convergence result.

## Poster.pdf

- Categories:

- Read more about ON THE CONVERGENCE OF CONSTRAINED PARTICLE FILTERS
- Log in to post comments

The power of particle filters in tracking the state of non-linear and non-Gaussian systems stems not only from their simple numerical implementation but also from their optimality and convergence properties. In particle filtering, the posterior distribution of the state is approximated by a discrete mass of samples, called particles, that stochastically evolve in time according to the dynamics of the model and the observations. Particle filters have been shown to converge almost surely toward the optimal filter as the number of particles increases.

- Categories: