Hadi Jamali-Rad

Sparsity-Aware Sensor Selection: Centralized and Distributed Algorithms

The selection of the minimum number of sensors within a network to satisfy a certain estimation performance metric is an interesting problem with a plethora of applications. We explore the sparsity embedded within the problem and propose a relaxed sparsity-aware sensor selection approach which is equivalent to the unrelaxed problem under certain conditions. We also present a reasonably low-complexity and elegant distributed version of the centralized problem with convergence guarantees such that each sensor can decide itself whether it should contribute to the estimation or not. Our simulation results corroborate our claims and illustrate a promising performance for the proposed centralized and distributed algorithms.

Sparsity-Aware Multi-Source TDOA Localization

The problem of source localization from time-difference-of-arrival (TDOA) measurements is in general a non-convex and complex problem due to its hyperbolic nature. This problem becomes even more complicated for the case of multi-source localization where TDOAs should be assigned to their respective sources. We simplify this problem to an ℓ1-norm minimization by introducing a novel TDOA fingerprinting and grid design model for a multi-source scenario. Moreover, we propose an innovative trick to enhance the performance of our proposed fingerprinting model in terms of the number of identifiable sources. An interesting by-product of this enhanced model is that under some conditions we can convert the given underdetermined problem to an overdetermined one that could be solved using classical least squares (LS). Finally, we also tackle the problem of off-grid source localization as a case of grid mismatch. Our extensive simulation results illustrate a good performance for the introduced TDOA fingerprinting paradigm as well as a significant detection gain for the enhanced model.

Target Localization and Tracking for an Isogradient Sound Speed Profile

In an underwater medium the sound speed is not constant, but varies with depth. This phenomenon upsets the linear dependency of the distance traveled by an acoustic wave to the time it takes for the wave to travel that distance, and therefore makes existing distance-based localization algorithms less effective in an underwater environment. This paper addresses the problems of localizing a fixed node and tracking a mobile target from acoustic time-of-flight (ToF) measurements in a three-dimensional underwater environment with an isogradient sound speed profile. To solve these problems we first analytically relate the acoustic wave ToF between two nodes to their positions. After obtaining sufficient ToF measurements, we then adopt the Gauss-Newton algorithm to localize the fixed node in an iterative manner, and we utilize the extended Kalman filter for tracking the mobile target in a recursive manner. Through several simulations, we will illustrate that the proposed algorithms perform superb since they meet the Cramér-Rao bound (CRB) for localization and posterior CRB for tracking.

Primal Recovery from Consensus-Based Dual Decomposition for Distributed Convex Optimization

Dual decomposition has been successfully employed in a variety of distributed convex optimization problems solved by a network of computing and communicating nodes. Often, when the cost function is separable but the constraints are coupled, the dual decomposition scheme involves local parallel subgradient calculations and a global subgradient update performed by a master node. In this paper, we propose a consensus-based dual decomposition to remove the need for such a master node and still enable the computing nodes to generate an approximate dual solution for the underlying convex optimization problem. In addition, we provide a primal recovery mechanism to allow the nodes to have access to approximate near-optimal primal solutions. Our scheme is based on a constant stepsize choice, and the dual and primal objective convergence are achieved up to a bounded error floor dependent on the stepsize and on the number of consensus steps among the nodes.

Dynamic Multidimensional Scaling for Low-Complexity Mobile Network Tracking

Cooperative localization of mobile sensor networks is a fundamental problem which becomes challenging for anchorless networks where there is no pre-existing infrastructure to rely on. Two cooperative mobile network tracking algorithms based on novel dynamic multidimensional scaling (MDS) ideas are proposed. The algorithms are also extended to operate in partially connected networks. Compared with recently proposed algorithms based on the extended and unscented Kalman filter (EKF and UKF), the proposed algorithms have a considerably lower computational complexity. Furthermore, model-independence, scalability, as well as an acceptable accuracy make our proposed algorithms a good choice for practical mobile network tracking.

Distributed Sparsity-Aware Sensor Selection

The selection of the minimum number of sensors within a network to satisfy a certain estimation performance metric is an interesting problem with a plethora of applications. The problem becomes even more interesting in a distributed configuration when each sensor has to decide itself whether it should contribute to the estimation or not. In this paper, we explore the sparsity embedded within the problem and propose a sparsity-aware sensor selection paradigm for both uncorrelated and correlated noise experienced at different sensors. We also present reasonably low-complexity and elegant distributed algorithms in order to solve the centralized problems with convergence guarantees within a bounded error. Furthermore, we analytically quantify the complexity of the distributed algorithms compared to centralized ones. Our simulation results corroborate our claims and illustrate a promising performance for the proposed centralized and distributed algorithms.

Sparsity-aware multi-source RSS localization

We tackle the problem of localizing multiple sources in multipath environments using received signal strength (RSS) measurements. The existing sparsity-aware fingerprinting approaches only use the RSS measurements (autocorrelations) at different access points (APs) separately and ignore the potential information present in the cross-correlations of the received signals. We propose to reformulate this problem to exploit this information by introducing a novel fingerprinting paradigm which leads to a significant gain in terms of number of identifiable sources. Besides, we further enhance this newly proposed approach by incorporating the information present in the other time lags of the autocorrelation and cross-correlation functions. An interesting by-product of the proposed approaches is that under some conditions we can convert the given underdetermined problem to an overdetermined one and efficiently solve it using classical least squares (LS). Moreover, we also approach the problem from a frequency-domain perspective and propose a method which is blind to the statistics of the source signals. Finally, we incorporate the so-called concept of finite-alphabet sparsity in our framework for the case where the sources have a similar power. Our extensive simulation results illustrate a good performance as well as a significant detection gain for the introduced multi-source RSS fingerprinting methods. HighlightsWe tackle the problem of localizing multiple sources in multipath environments.We exploit the information present in the cross-correlations using a novel fingerprinting.We enhance our proposed approach by incorporating the other time lags of the correlation functions.We also present a method which is blind to the statistics of the source signals.Our approaches lead to a significant gain in terms of number of identifiable sources.

Sparse multi-target localization using cooperative access points

In this paper, a novel multi-target sparse localization (SL) algorithm based on compressive sampling (CS) is proposed. Different from the existing literature for target counting and localization where signal/received-signal-strength (RSS) readings at different access points (APs) are used separately, we propose to reformulate the SL problem so that we can make use of the cross-correlations of the signal readings at different APs. We analytically show that this new framework can provide a considerable amount of extra information compared to classical SL algorithms. We further highlight that in some cases this extra information converts the under-determined problem of SL into an over-determined problem for which we can use ordinary least-squares (LS) to efficiently recover the target vector even if it is not sparse. Our simulation results illustrate that compared to classical SL this extra information leads to a considerable improvement in terms of number of localizable targets as well as localization accuracy.

Sparsity-aware TDOA localization of multiple sources

The problem of source localization from time-difference-of-arrival (TDOA) measurements is in general a non-convex and complex problem due to its hyperbolic nature. This problem becomes even more complicated for the case of multi-source localization where TDOAs should be assigned to their respective sources. We simplify this problem to an ℓ1-norm minimization by introducing a novel TDOA fingerprinting model for a multi-source scenario. Moreover, we propose an innovative trick to enhance the performance of our proposed fingerprinting model in terms of the number of identifiable sources. An interesting by-product of this enhanced model is that under some conditions we can convert the given underdetermined problem to an overdetermined one and efficiently solve it using classical least squares (LS) approaches. Our simulation results illustrate a good performance for the introduced TDOA fingerprinting.

Cooperative localization in partially connected mobile wireless sensor networks using geometric link reconstruction

We extend one of our recently proposed anchorless mobile network localization algorithms (called PEST) to operate in a partially connected network. To this aim, we propose a geometric missing link reconstruction algorithm for noisy scenarios and repeat the proposed algorithm in a local-to-global fashion to reconstruct a complete distance matrix. This reconstructed matrix is then used in the PEST to localize the mobile network. We compare the computational complexity of the new link reconstruction algorithm with existing related algorithms and show that our proposed algorithm has the lowest complexity, and hence, is the best extension of the low complexity PEST. Simulation results further illustrate that the proposed link reconstruction algorithm leads to the lowest reconstruction error as well as the most accurate network localization performance.