Thuraiappah Sathyan

WASP: A System and Algorithms for Accurate Radio Localization Using Low-Cost Hardware

In this paper, we present a low-cost wireless sensor network platform, called wireless ad hoc system for positioning (WASP), that has been developed for high-accuracy localization and tracking. This platform uses the time of arrival (TOA) of beacon signals periodically transmitted by the nodes at known times for localization. The system was designed to have a unique tradeoff between hardware complexity and processing complexity to provide high accuracy at minimal cost in complex radio propagation environments. To enable the system to perform well in realistic environments, it was also necessary to develop novel extensions to existing algorithms for the measurement of TOA, localization, and tracking. In this paper, we describe the architecture, hardware, and algorithms of WASP and present results based on field trials conducted in different radio propagation environments. The results show that WASP achieves a ranging accuracy of 0.15 m outdoors and 0.5 m indoors when around 12 anchor nodes are used. These accuracies are achieved with operating range of up to 200 m outdoors and 30 m indoors. This compares favorably to other published results for systems operating in realistic environments.

Passive geolocation and tracking of an unknown number of emitters

An algorithm for the geolocation and tracking of an unknown number of ground emitters using the time difference of arrival (TDOA) measurements in practical scenarios is proposed. The focus is on solving the important issue of data association, i.e., deciding from which target, if any, a measurement originated. A previous solution for data association based on the assignment formulation for passive measurement tracking systems relied on solving two assignment problems: an S-dimensional (or SD, where S /spl ges/ 3) assignment for association across sensors and a 2D assignment for the measurement-to-track association. In this paper, (S + 1 )D assignment algorithm - an extension of the SD assignment formulation - that performs the data association in one step, is introduced. It will be shown later that the (S + 1 )D assignment formulation reduces the computational cost significantly without compromising tracking accuracy. The incorporation of correlated measurements, as with the case of TDOA measurements, into the SO framework that typically assumes uncorrelated measurements, is also discussed. The nonlinear TDOA equations are posed as an optimization problem and solved using SolvOpt, a nonlinear optimization solver. The interacting multiple model (IMM) estimator is used in conjunction with the unscented Kalman filter (UKF) to track the geolocated emitters.

Positional Accuracy Measurement and Error Modeling for Mobile Tracking

This paper presents a method of determining the statistical positional accuracy of a moving object being tracked by any 2D (but particularly radiolocation) positioning system without requiring a more accurate reference system. Commonly for testing performance only static positional errors are measured, but typically for radiolocation systems the positional performance is significantly different for moving objects compared with stationary objects. When only the overall statistical performance is required, the paper describes a measurement technique based on determining 1D cross-track errors from a nominal path, and then using this data set to determine the overall 2D positional error statistics. Comparison with simulated data shows that the method has good accuracy. The method is also tested with vehicle tracking in a city and people tracking within a building. For the indoor case, static and dynamic measurements allowed the degrading effect of body-worn devices due to signal blockage to be determined. Error modeling is also performed and a Rayleigh-Gamma model is proposed to describe the radial positional errors. It is shown that this model has a good match with both indoor and outdoor field measurements.

Markov-jump-system-based secure chaotic communication

In this paper, a new Markov-jump-system (MJS)-based secure chaotic communication technique is proposed. An MJS evolves by switching from one state evolution model to another according to a finite state Markov chain. The transmitter in the proposed communication system is an MJS consisting of multiple transmission maps, that is, the transmitter switches from one chaotic map to another during the transmission of data. This switching feature makes it difficult to identify and follow the transmission without knowing the transmitter parameters, i.e., to eavesdrop, thereby increasing the security offered by the inherently secure chaotic communication system. If the chaotic maps used at the transmitter, and the corresponding Markov transition probability matrix of the MJS are known to the (authorized) receiver, then a multiple model estimator can be used to track the MJS transmitter. In this paper, the use of the interacting multiple model (IMM) estimator is proposed as part of the receiver to follow the switching transmitter. The effectiveness of the IMM-estimator-based receiver to follow the switching transmitter is evaluated by means of simulations. A new modulation technique that uses the MJS transmitter is also introduced. Further, it is shown that the same receiver framework, when used as a receiver for chaotic parameter modulation, provides significant performance improvement in terms of bit-error rate compared to a receiver that uses extended Kalman filter. In addition, the seemingly more complex IMM-estimator-based receiver is shown to significantly reduce the computational complexity per transmitted bit, thus resulting in increased data rate.

A Two-Stage Assignment-Based Algorithm for Asynchronous Multisensor Bearings-Only Tracking

We present a two-stage centralized algorithm for tracking multiple targets using spatially distributed bearings-only sensors that report the observations asynchronously. The sensors are assumed to be passive, i.e., they detect the energy emitted by the targets of interest to measure the bearing. The number of targets in the surveillance region is unknown a priori and the targets can enter or leave the surveillance region at any time. The measurement origin is also unknown since a detection can be due to the target being tracked, from a new target, or from clutter. In the first stage (the initialization stage) the proposed algorithm forms local bearings-only (mono) tracks for each sensor and combines these tracks to generate complete kinematic (stereo) tracks in the Cartesian coordinate frame. Once stereo tracks are formed, in the second stage, which is called the stereo tracking stage, bearing measurements are directly used to update the stereo tracks. This separation of the initialization and maintenance of the stereo tracks is lacking in many existing algorithms and results in improved performance. In this work we used the assignment-based technique to solve various data association problems that arise due to measurement origin uncertainty. Through extensive simulations we show that the proposed algorithm achieves better tracking accuracy while being computationally simpler than existing algorithms.

A manifold flattening approach for anchorless localization

We present a new method for anchorless localization of mobile nodes in wireless networks using only measured distances between pairs of nodes. Our method relies on the completion of the Euclidean distance matrix, followed by multidimensional scaling in order to compute the relative locations of the nodes. The key element of novelty of our algorithm is the method of completing the Euclidean distance matrix, which consists of gradually inferring the unknown distances, such as to align all nodes on a k-hyperplane, where typically k is 2 or 3. Our method leads to perfect anchorless localization for noise-free range measurements, if the network is sufficiently connected. We introduce refinements to the algorithm to make it robust to noisy and outlier range measurements. We present results from several localization tests, using both simulated data and experimental results measured using a large indoor network deployment of our WASP platform. Our results show improvements in localization using our algorithm over previously published techniques.

MDA-Based Data Association with Prior Track Information for Passive Multitarget Tracking

An assignment-based solution for the data association problem in synchronous passive multisensor (Type 3) tracking systems involves two steps: first measurement-to-measurement or static association is solved using a multidimensional (S-dimensional or S-D with S sensors) assignment, and then measurement-to-track association is solved using a 2-D assignment. This solution is computationally very expensive and, to rectify an efficient (S+1)-D assignment algorithm has been proposed in the literature. Two new assignment-based algorithms are proposed that use prior track information (i.e., predicted state and covariance) which result in improved tracking performance compared with the existing solutions, while requiring considerably less computations. One of the proposed algorithms, the gated assignment, is similar to the two-step solution mentioned above except that it uses prior track information and avoids the need to consider all possible association hypotheses in the static association step. The second algorithm, the gated (S+1)-D assignment, combines the gated assignment and the (S+1)-D algorithms. An approximation to the (S+1)-D algorithm is also derived when sensor measurements are independent, which results in an extremely fast solution. Simulation results confirm that the proposed algorithms show improved tracking performance and faster execution times.

An analysis of the error characteristics of two time of arrival localization techniques

Accurate local positioning systems usually use a network of anchor nodes at known locations to track mobile nodes based on the measurement of the time of arrival (TOA) at anchor nodes of beacon signals transmitted by the mobile nodes. To localize the mobile node either TOA processing, where the unknown transmit time is estimated along with the node location, or time difference of arrival (TDOA) processing, where the transmit time is eliminated before estimating the node location, can be used. We show that the position error bound of both these formulations are the same by analyzing the Cramér-Rao lower bound. When processing data collected in field trials, however, we observed that the TOA processing yields better localization accuracy, and explain this behavior using differential geometry-based curvature measures that show that the TDOA cost function has greater degree of non-linearity.

Range-Only Tracking in Multipath Environments - An Algorithm Based on Particle Filtering

In complex propagation environments with multipath reflections determining the time-of-arrival (TOA) of the line-of-sight (LoS) signal, which is required for localization, is challenging. As a result the localization accuracy of TOA-based systems degrade in such environments. We are pursuing a novel approach where the TOA algorithm returns not one but multiple candidate TOA values as this list is more likely to contain the LoS TOA value and the localization algorithm uses all of these to form a more reliable estimate of the node location. In this paper we present a new algorithm for localization and tracking based on multiple candidate TOA values from each anchor. We show that this is similar to the well-known data association problem in target tracking and exploit this similarity to propose a particle filter based algorithm that has linear computational complexity in the number of anchors. The performance of the algorithm is validated using simulated data.

Efficient particle filtering for tracking maneuvering objects

Accurate tracking of elite athletes for performance monitoring allows sports scientists to optimize training to gain a competitive edge. An important challenge in this application is that the maneuverability of the athletes is high and the traditional Kalman filter (KF) will not provide satisfactory tracking accuracy. Further, high update rates, of the order of tens of updates per second for each player, are often required and hence, the tracking algorithm considered should be computationally efficient. In this paper we propose a computationally efficient multiple model particle filter (MM-PF) algorithm for tracking maneuvering objects. It uses a Gaussian proposal density based on the unscented KF and a deterministic sampling technique and provides tracking accuracy similar to that of the augmented MM-PF, but with much lower computational cost. The performance of the proposed algorithm was verified using simulations and data collected in field trials. The trials were conducted with the Australian Institute of Sport using a localization system we have designed.