Hamid Ramezani

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.

Ranging in an underwater medium with multiple isogradient sound speed profile layers

In this paper, we analyze the problem of acoustic ranging between sensor nodes in an underwater environment. The underwater medium is assumed to be composed of multiple isogradient sound speed profile (SSP) layers where in each layer the sound speed is linearly related to the depth. Furthermore, each sensor node is able to measure its depth and can exchange this information with other nodes. Under these assumptions, we first show how the problem of underwater localization can be converted to the traditional range-based terrestrial localization problem when the depth information of the nodes is known a priori. Second, we relate the pair-wise time of flight (ToF) measurements between the nodes to their positions. Next, based on this relation, we propose a novel ranging algorithm for an underwater medium. The proposed ranging algorithm considers reflections from the seabed and sea surface. We will show that even without any reflections, the transmitted signal may travel through more than one path between two given nodes. The proposed algorithm analyzes them and selects the fastest one (first arrival path) based on the measured ToF and the nodes’ depth measurements. Finally, in order to evaluate the performance of the proposed algorithm we run several simulations and compare the results with other existing 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.

L-MAC: Localization packet scheduling for an underwater acoustic sensor network

This article concerns the problem of scheduling the localization packets of the anchors in an underwater acoustic sensor network (UASN). Knowing the relative positions of the anchors and their maximum transmission range, we take advantage of the long propagation delay of underwater communication to minimize the duration of the localization task. First, we formulate the concept of collision-free packet transmission for localization, and we show how the optimum solution can be obtained. Furthermore, we propose two low-complexity algorithms, and through comprehensive simulations we compare their performances with the optimal solution as well as other existing methods. Numerical results show that the proposed algorithms perform near optimum and better than alternative solutions.

Collision Tolerant and Collision Free Packet Scheduling for Underwater Acoustic Localization

This article considers the joint problem of packet scheduling and self-localization in an underwater acoustic sensor network with randomly distributed nodes. In terms of packet scheduling, our goal is to minimize the localization time, and to do so we consider two packet transmission schemes, namely a collision-free scheme (CFS), and a collision-tolerant scheme (CTS). The required localization time is formulated for these schemes, and through analytical results and numerical examples their performances are shown to be dependent on the circumstances. When the packet duration is short (as is the case for a localization packet), the operating area is large (above 3 km in at least one dimension), and the average probability of packet-loss is not close to zero, the collision-tolerant scheme is found to require a shorter localization time. At the same time, its implementation complexity is lower than that of the collision-free scheme, because in CTS, the anchors work independently. CTS consumes slightly more energy to make up for packet collisions, but it is shown to provide a better localization accuracy. An iterative Gauss-Newton algorithm is employed by each sensor node for self-localization, and the Cramér Rao lower bound is evaluated as a benchmark.

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.

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.

Packet scheduling for underwater acoustic sensor network localization

This article considers the problem of packet scheduling for localization in an underwater acoustic sensor network where sensor nodes are distributed randomly in an operating area. Our goal is to minimize the localization time, and to do so we consider two packet transmission schemes, namely collision-free, and collision-tolerant. Through analytical results and numerical examples the performances of these schemes are shown to be comparable. In general, for small packet length (as is the case for a localization packet) and large operating area (above 3km in at least one dimension), the performances of the collision-tolerant protocol is superior to its collision-free counterpart. At the same time, the anchors work independently of each other, and this feature simplifies the implementation process.

Localization and tracking of a mobile target for an isogradient sound speed profile

In this paper, we analyze the problem of localizing and tracking a mobile node in an underwater environment with an isogradient sound speed profile (SSP). We will show that range-based localization algorithms are not so accurate in such an environment, and they should be replaced by time-based ones. Therefore, we relate the mobile node location to the travel time of a propagating sound wave from (to) an anchor node to (from) the mobile node. After obtaining sufficient time measurements, positioning can be achieved through multilateration. To accomplish this, we utilize the extended Kalman filter (EKF) for multilateration and tracking the mobile nodes location in a recursive manner. Through several simulations, we will show that the proposed EKF algorithm performs superb in comparison with algorithms which assume a straight-line wave propagation in an underwater environment.

Localization Packet Scheduling for Underwater Acoustic Sensor Networks

Medium access control (MAC) determines how sensor nodes share the channel for packet exchanging. To obtain the maximum network efficiency for accomplishing a specific task, the network has to adapt its parameters accordingly. In other words, different MAC protocols are required for different tasks. Localization is a crucial task of an underwater acoustic sensor network (UASN) which requires multiple packet exchanges. This article concerns the problem of designing a MAC protocol for a UASN which efficiently schedules the localization packets of the anchors. Knowing the relative positions of the anchors and their maximum transmission range, the scheduling protocol takes advantage of the long propagation delay of underwater communications to minimize the duration of the localization task. First, we formulate the concept of collision-free packet transmission for localization, and we show how the optimum solution can be obtained. Furthermore, we model the problem as a mixed integer linear program both in single-channel and multi-channel scenarios. Then, we propose two low-complexity algorithms, and through comprehensive simulations we compare their performances with the optimal solution as well as with other existing methods. Numerical results show that the proposed algorithms perform near optimum and better than alternative solutions.