Alon Amar

Bio: Alon Amar is an academic researcher from Technion – Israel Institute of Technology. The author has contributed to research in topics: Underwater acoustic communication & Estimator. The author has an hindex of 13, co-authored 37 publications receiving 799 citations. Previous affiliations of Alon Amar include Tel Aviv University & Rafael Advanced Defense Systems.

Localization of Narrowband Radio Emitters Based on Doppler Frequency Shifts

Abstract: Several techniques for emitter localization based on the Doppler effect have been described in the literature. One example is the differential Doppler (DD) method in which the signal of a stationary emitter is intercepted by at least two moving receivers. The frequency difference between the receivers is measured at several locations along their trajectories and the emitter's position is then estimated based on these measurements. This two-step approach is suboptimal since each frequency difference measurement is performed independently, although all measurements correspond to a common emitter position. Instead, a single-step approach based on the maximum likelihood criterion is proposed here for both known and unknown waveforms. The position is determined directly from all the observations by a search in the position space. The method can only be used for narrowband signals, that is, under the assumption that the signal bandwidth must be small compared to the inverse of the propagation time between the receivers. Simulations show that the proposed method outperforms the DD method for weak signals while both methods converge to the Cramer-Rao bound for strong known signals. Finally, it is shown that in some cases of interest the proposed method inherently selects reliable observations while ignoring unreliable data.

Direct position determination of multiple radio signals

Abstract: The most common methods for position determination of radio signal emitters such as communications or radar transmitters are based on measuring a specified parameter such as angle of arrival (AOA) or time of arrival (TOA) of the signal. The measured parameters are then used to estimate the transmitter's location. Since the measurements are done at each base station independently, without using the constraint that the AOA/TOA estimates at different base stations should correspond to the same transmitter's location, this is a suboptimal location determination technique. Further, if the number of array elements at each base station is M, and the signal waveforms are unknown, the number of cochannel simultaneous transmitters that can be localized by AOA is limited to M - 1. Also, most AOA algorithms fail when the sources are not well angularly separated. We propose a technique that uses exactly the same data as the common AOA methods but the position determination is direct. The proposed method can handle more than M - 1 cochannel simultaneous signals. Although there are many stray parameters, only a two-dimensional search is required for a planar geometry. The technique provides a natural solution to the measurements sources association problem that is encountered in AOA-based location systems. In addition to new algorithms, we provide analytical performance analysis, Cramer-Rao bounds and Monte Carlo simulations. We demonstrate that the proposed approach frequently outperforms the traditional AOA methods for unknown as well as known signal waveforms.

Direct position determination of multiple radio signals

Abstract: The most common methods for localization of radio signal emitters are based on measuring a specified parameter such as signal angle-of-arrival (AOA) or time-of-arrival (TOA). The measured parameters are then used to estimate the transmitter location. Since the measurements are done at each base station independently, without using the constraint that all AOA/TOA estimates must correspond to the same transmitter, they are sub-optimal. Moreover, if the number of array elements at each base station is M, the number of cochannel simultaneous transmitters that can be localized is M-1. We propose a technique that uses exactly the same data as the common AOA/TOA methods, but the position determination is direct. The proposed method can handle LM-1 cochannel simultaneous signals, using L base stations. Although there are many stray parameters, only a two-dimensional search is required for a planar geometry and a three-dimensional search for the general case. The technique exploits the principles of the MUSIC algorithm, and provides a natural solution to the measurements-sources association problem that is encountered in AOA/TOA based systems.

Fundamental Limitations on the Resolution of Deterministic Signals

Abstract: We exploit results from detection theory for deriving fundamental limitations on resolution. The results are general and are not based on any specific resolution technique and therefore hold for any method and for any resolution success rate. We show that for signals with additive white Gaussian noise the resolution limit is approximated by a simple expression related to the signal to noise ratio, the signal waveform and the resolution success rate. As an example, we discuss the resolution of two complex exponentials with closely spaced frequencies. The theoretical result is compared with the empirical performance of model order selection methods such as the Akaike information criterion and the minimum description length.

ToneTrack: Leveraging Frequency-Agile Radios for Time-Based Indoor Wireless Localization

Abstract: Indoor localization of mobile devices and tags has received much attention recently, with encouraging fine-grained localization results available with enough line-of-sight coverage and hardware infrastructure. Some of the most promising techniques analyze the time-of-arrival of incoming signals, but the limited bandwidth available to most wireless transmissions fundamentally constrains their resolution. Frequency-agile wireless networks utilize bandwidths of varying sizes and locations in a wireless band to efficiently share the wireless medium between users. ToneTrack is an indoor location system that achieves sub-meter accuracy with minimal hardware and antennas, by leveraging frequency-agile wireless networks to increase the effective bandwidth. Our novel signal combination algorithm combines time-of-arrival data from different transmissions as a mobile device hops across different channels, approaching time resolutions previously not possible with a single narrowband channel. ToneTrack's novel channel combination and spectrum identification algorithms together with the triangle inequality scheme yield superior results even in non-line-of-sight scenarios with one to two walls separating client and APs and also in the case where the direct path from mobile client to an AP is completely blocked. We implement ToneTrack on the WARP hardware radio platform and use six of them served as APs to localize Wi-Fi clients in an indoor testbed over one floor of an office building. Experimental results show that ToneTrack can achieve a median 90 cm accuracy when 20 MHz bandwidth APs overhear three packets from adjacent channels.

Direct Localization for Massive MIMO

Abstract: Large-scale MIMO systems are well known for their advantages in communications, but they also have the potential for providing very accurate localization, thanks to their high angular resolution. A difficult problem arising indoors and outdoors is localizing users over multipath channels. Localization based on angle of arrival (AOA) generally involves a two-step procedure, where signals are first processed to obtain a user's AOA at different base stations, followed by triangulation to determine the user's position. In the presence of multipath, the performance of these methods is greatly degraded due to the inability to correctly detect and/or estimate the AOA of the line-of-sight (LOS) paths. To counter the limitations of this two-step procedure which is inherently suboptimal, we propose a direct localization approach in which the position of a user is localized by jointly processing the observations obtained at distributed massive MIMO base stations. Our approach is based on a novel compressed sensing framework that exploits channel properties to distinguish LOS from non-LOS signal paths, and leads to improved performance results compared to previous existing methods.

Bias Reduction for an Explicit Solution of Source Localization Using TDOA

Abstract: This paper proposes two methods to reduce the bias of the well-known algebraic explicit solution (Chan and Ho, "A simple and efficient estimator for hyperbolic location," IEEE Trans. Signal Process., vol. 42, pp. 1905-1915, Aug. 1994) for source localization using TDOA. Bias of a source location estimate is significant when the measurement noise is large and the geolocation geometry is poor. Bias also dominates performance when multiple times of independent measurements are available such as in UWB localization or in target tracking. The paper starts by deriving the bias of the source location estimate from Chan and Ho. The bias is found to be considerably larger than that of the Maximum Likelihood Estimator. Two methods, called BiasSub and BiasRed, are developed to reduce the bias. The BiasSub method subtracts the expected bias from the solution of Chan and Ho's work, where the expected bias is approximated by the theoretical bias using the estimated source location and noisy data measurements. The BiasRed method augments the equation error formulation and imposes a constraint to improve the source location estimate. The BiasSub method requires the exact knowledge of the noise covariance matrix and BiasRed only needs the structure of it. Analysis shows that both methods reduce the bias considerably and achieve the CRLB performance for distant source when the noise is Gaussian and small. The BiasSub method can nearly eliminate the bias and the BiasRed method is able to lower the bias to the same level as the Maximum Likelihood Estimator. The BiasRed method is extended for TDOA and FDOA positioning. Simulations corroborate the performance of the proposed methods.

A Theoretical Foundation of Network Localization and Navigation

Abstract: Network localization and navigation (NLN) is a promising paradigm, in which mobile nodes exploit spatiotemporal cooperation, to provide reliable location information for a diverse range of wireless applications. This paper presents a theoretical foundation of NLN, including a mathematical formulation for NLN, an introduction of equivalent Fisher information analysis, and determination of the fundamental limits of localization accuracy. Key ingredients such as spatiotemporal cooperation, array signal processing, and map exploitation are then studied. We also develop a geometric interpretation to provide insights into the essence of NLN for network design. Finally, the paper highlights the connection between the theoretical foundation and algorithm development for NLN, guiding the design and operation of practical localization systems.