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January 70-75, pp. 1, no. 59, vol. , r } @nyu.edu aal faheigarnigacrc nthe on accuracy typically ranging [4]. centimeters are a of 11 that achieving order UWB iPhone chips of utilizing the UWB capable with in carrying expected, strides currently are more Rapid localization of MHz. for bandwidth a 500 have band, than in GHz signals, (UWB), 3.1-10.6 UWB wideband the Bluetooth. ultra and light, as visible such RFID, media other using have infrared [3]. accuracy using good systems [2], shown accuracy factory-based sub-millimeter while with distances and vehicles relative preci- other the detection environment. to estimate a light office to achieve (LIDAR) an utilize ranging within to vehicles cm able 10 Autonomous is of are level visibility Forkbeard sion when systems as blocked positioning such not indoor Ultrasound are hampered. they bet- since work frequencies ter lo- cellular sen- environments, at systems inertial low-visibility calization with posi- in However, accurate fused provide sors. when can capabilities [1]) tioning drones by as utilized (commonly such cameras using technologies localization vision-based sensor-based other systems, 07-114.). Docket PS Order (see and barometric phones Report cell using Fifth modern FCC’s achievable in of caller’s present is the percent sensors identify pressure which 80 to level, for 2021, m April floor by 3 callers than vertical enhanced applications E911 a less positioning of requires error than for FCC percent localization Additionally, less required future. 80 error be the of localization will for m a FCC m 3 callers, Although 50 (E911) than meters. 911 less error of localization of horizontal tens a specify of providing requirements the strengths, accuracy weighted where signal the received an hear, from the can formed of is average UE all UE estimate of the position The positions UE that known Google. the hotspots and by using Wi-Fi localized used Apple be been as may have such hotspots companies Wi-Fi geo-tagged of h deto ilmtrwv mWv)com- (mmWave) millimeter-wave of advent The developed being are solutions location Position positioning infrastructure-based to addition In si ellrCmuiain Gand 5G - Communications Cellular istic munications enables a paradigm shift in localization capabilities by allowing joint communication and position location, utilizing the same infrastructure. As shown in this article, the massive bandwidths, coupled with the high gain directional, steerable Δ = − multiple-input multiple-output (MIMO) antennas 2 1 hyperbola at mmWave frequencies, enable unprecedented lo- calization accuracy in smartphones of the future. circle BS 1 We demonstrate how the utilization of cooperative BS 2 (x1,y1) (x2,y2) d2 ,2 localization, machine learning, user tracking, and d 1 ,3 multipath enables precise centimeter-level position 1 location. d3 Δ 1 − 3 UE (x,y) BS 3 II. FUNDAMENTAL LOCALIZATION (x3,y3) TECHNIQUES Fig. 1. The UE may be localized based on ToA (black circles), Today’s localization solutions primarily focus on TDoA (red hyperbola), or AoA (black dotted lines) localization geometric localization with augmented assistance, techniques [5]. wherein the position of the base station (BS) is known and the UE location is determined based on geometric constraints such as the BS-UE distances The short wavelength in the mmWave frequency and physical angular orientations between BS and band allows electrically large (but physically small) UE. antenna arrays to be deployed at both the UE and In angle of arrival (AoA) localization technique, BS. MmWave BS antenna arrays with 256 antenna the UE estimates the angle of the strongest re- elements and 32-element mobile antenna arrays ceived signal. AoA positioning was conceived for are already commercially available. The frequency- E911 in the early days of cellular [5]. In time independent half-power beamwidth (HPBW) of a of arrival (ToA) (or time difference of arrival, uniform rectangular array (URA) antenna with TDoA) localization techniques, the UE estimates half-wavelength element spacing is approximately the distance (or difference in distance) from the (102/N)°, where N is the number of antenna ele- BS by estimating travel time (or differences in ments in each linear dimension of the planar array travel time) of the reference signal from the BS. [8], as seen in Fig. 2. The UE may then be localized to the point where Narrower HPBWs of antenna arrays allow the the circles (or hyperbolas) corresponding to the AoA of received signals to be estimated precisely, BS-UE distances intersect. A spatial resolution of and further signal processing provides better accu- up to 2.44 m and 4.88 m is achievable with 5G racy. For example, the sum-and-difference for an New Radio (NR) waveforms for ToA and TDoA infrared system technique achieved sub-degree an- measurements, respectively [6]. In addition to uti- gular resolution with two overlapping and slightly lizing GPS for UE localization, 4G (and future 5G) offset antenna arrays [3], showing it is possible to networks implement TDoA localization and utilize very accurately detect precise AoA at UEs or BSs. the barometric pressure sensors in UE for altitude Although mmWave frequencies suffer from estimation [1]. The operation of AoA, ToA, and higher path loss in the first meter of propagation TDoA localization techniques is illustrated in Fig. and experience greater blockage losses compared 1 and is well understood. to lower frequencies, the greater gain provided by the directional antennas coupled with smaller A. Accurate Localization in 5G Networks with Di- serving cells (100-200 m radius) compensates for rectional Antenna Arrays and Wide Bandwidths the additional path loss. Indeed, recent research [9] In the 5G era, it is now possible to achieve very demonstrates the feasibility of using mmWave for accurate localization performance with highly di- outdoor localization. rectional antenna arrays having narrow beamwidths Utilization of mmWave frequency bands will and wide bandwidths [7]. The frequency range (FR) enable unprecedented positioning accuracy due to 2 of 5G NR covers mmWave frequencies ranging the ultra-wide bandwidths available, since the larger from 24.25 GHz to 52.6 GHz. Additionally, the bandwidths allow finer time resolution of multipath IEEE 802.11 ad standard supports the use of the signals transmitted from the BS to the UE, on the 60 GHz mmWave band indoors, from 57 GHz to order of a nanosecond, where a 1 ns time resolution 71 GHz. implies a spatial resolution of 30 cm before addi- more than 5° when the UE was moved by 5 cm, the signal was assumed to correspond to an NLoS path and thus discarded from use in estimating position. By suppressing NLoS multipath and only using the LoS path, a median localization accuracy of 23 cm was achieved with six 2.4 GHz WiFi access points [11]. Estimating the BS-UE distance, a critical step for ToA localization, may additionally be utilized to determine whether the BS-UE link is in NLoS. The running variance of the BS-UE distance estimates Fig. 2. The normalized antenna gain (with respect to boresight - 2 the axis of maximum gain) of URAs with 8×8, 16×16, 32×32, and (σ ) in NLoS is greater than LoS; hence, NLoS BS- 64×64 array elements. Note the half power beamwidths (HPBWs) UE links may be identified based on the running are 12.76°, 6.34°, 3.17°, and 1.55° respectively. variance observed in real time. The UE can accu- rately be assumed to be in NLoS (and the UE-BS link is not used for localization) when σ2 is greater tional processing that can further improve accuracy. than a calibrated threshold γ [12]. The variance of distance estimates is greater for a mobile user B. Performance of Fundamental Localization Tech- than for a stationary user due to the change in the niques in Dense Multipath Environments true BS-UE distance when the UE is in motion. To ToA, TDoA, and AoA localization techniques account for user motion, γ must be increased, and were designed for line-of-sight (LoS) propagation. in [12], a constant proportional to the square of the In indoor/outdoor non-line-of-sight (NLoS) envi- velocity of the user was added to γ to account for ronments however, multipath arrives at different an- user motion. gles with larger delays, yielding positioning error. Without using any advanced correction techniques, Channel features such as maximum received a poor mean error of 10 m was observed with well- power, root mean square (RMS) delay spread, known AoA localization based on NLoS indoor Rician-K factor, and the angular spread of depar- office measurements [10]. Similar enormous mean ture/arrival may be utilized to determine whether errors of 8-10 m inside buildings were observed the UE is in NLoS [13]. NLoS channels typically in NLoS when the localization performance was have lower maximum received power over the tested using traditional methods from outdoor E- power delay profile (PDP) due to the presence of 911 [5] via simulations in NYURay, a 3D mmWave obstructions and reflectors. The delay spread of ray tracer [7]. The poor localization accuracy of multipath components is higher in NLoS environ- known approaches, in the face of multipath and ments. The K-factor of a channel is equal to the an obstructed or weak LoS signal, motivates the ratio of the square of the peak amplitude of the need to develop more accurate and robust local- dominant signal and the variance in the channel ization approaches that exploit the wide bandwidth amplitude and is known to indicate the degree of and narrow beamwidths of 5G and beyond for multipath in a signal [13]. In NLoS channels, due multipath-rich NLoS environments. to the absence of a direct path, the K-factor is close to 0 dB. The angular spread of NLoS channels is C. NLoS Mitigation for Accurate Positioning wider since the multipath components arrive from varied directions. To combat the poor performance of traditional ToA-, TDoA-, and AOA-based localization tech- NLoS classification accuracy is improved when niques in NLoS environments, NLoS mitigation multiple channel characteristics are used in tan- techniques can identify and then discard NLoS dem [13]. A support vector machine (SVM) is a signals to only use the LoS BSs for localization. popular classifier capable of classifying data based This subsection describes a variety of techniques to on multiple parameters. An SVM utilizes channel selectively identify and discard the NLoS signals. characteristics to determine a hyperplane, which In [11] the authors observed that with conven- divides data into two classes. For NLoS identifica- tional WiFi radios operating at 2.4 GHz, the AoA tion, the SVM determines the optimal hyperplane to was stable over small UE movements (5 cm) in LoS divide data into LoS and NLoS classes. In [13], an environments, while in NLoS environments, the SVM was shown to outperform individual channel AoA varied by more than 5° if the UE was moved features, reducing the NLoS identification error rate by 5 cm. If the AoA of the received power varied by from 10 percent to 5 percent. III. SUB-METER PRECISE POSITION LOCATION environment with centralized and distributed coop- erative localization, respectively, over an area of Identifying and discarding NLoS signals to only approximately 40 m × 20 m with four BS with use LoS signals for localization wastes multipath known locations and 13 unknown UE locations [1]. signal energy, and requires dense BS deployment since the UE must be in LoS of two or more BSs B. Machine Learning for Localization for classical LoS positioning techniques to work. However, such over-deployment of BSs may be In contrast to geometry-based localization algo- cost-prohibitive. We shall now look at alternative rithms, machine learning provides a data-centric localization techniques wherein the UE utilizes view of the UE localization problem. Localization information from neighboring UEs, and exploits algorithms that employ machine learning first cre- NLoS BSs, and multipath. ate a “fingerprinting database” of the environment during the training (offline) phase [9]. A fingerprint A. Cooperative Localization is a vector containing channel parameters such as the received signal strength (RSS), channel state With the introduction of device-to-device (D2D) information (CSI), and the AoA of the strongest communication protocols in 5G [1], an exciting signal of all BS links measured a priori at known lo- avenue for cooperative localization has opened up. cations called reference points, distributed through- UEs may now directly communicate with one an- out the environment. A fingerprinting database is other instead of communicating with the BS alone constructed by storing the fingerprint measured at in order to achieve localization of all UE. each reference point with the coordinates of the Due to dedicated communication resources al- reference point. located for D2D communication in 5G, UEs may Once the fingerprinting database is constructed, conduct range and angular measurements on each then in the real-time online position location step D2D link. Since UEs are typically located closer the BS-UE channel is measured by the UE. The to one another than to BSs, the probability of D2D channel measurements are matched to the finger- links being LoS and having higher signal-to-noise printing database (stored in the UE or in the ratio (SNR) is greater, providing better positioning N network) to determine the UE position. Matching accuracy. In a network with N UEs, up to 2
may be done via maximum a posteriori (MAP) additional D2D link measurements are possible. estimation. The relative UE location information, extracted Alternatively, matching may be performed by from the D2D link measurements, may be sent utilizing a similarity criterion to compare the online to a central localization unit co-located at one of measurements to the fingerprinting database. A the serving BS or a central server (i.e., centralized common similarity criterion is the distance, such as cooperative localization). The position of all the the Euclidean (L2) or the Manhattan (L1) distance, UEs in the network is simultaneously determined of the online measurements from the channel mea- by nonlinear least squares (LS) estimation, wherein surement at the reference points. In the k-nearest the positions of the UEs that jointly minimize neighbor (k-NN) algorithm, the user position is the deviation from the physical angular orientation the weighted average of the k “nearest” reference and distance-based link constraints are determined. points. Optimization techniques such as the Levenberg- The UE localization problem can be restated as Marquardt algorithm (LMA) [14], which combines determining the nonlinear function that transforms the Gauss-Newton algorithm and the method of the channel parameters into a position estimate. gradient descent, may be used for nonlinear LS A neural network determines the nonlinear func- estimation. tion, based on data available in the fingerprinting Centralized cooperative localization in future database. A neural network is a series of multi-level dense IoT networks may lead to network conges- nonlinear functional transformations of the input, tion if all localization messages are routed to a which can be used to approximate a target func- central server. In distributed algorithms, UEs are tion. For user localization, the inputs to the neural localized based on local measurements exchanged network are the measured channel parameters, and by neighboring nodes (as is done in centralized the target function is the positional coordinates of localization). The location estimates of the UEs the user. Successive layers of a neural network are are then iteratively refined until all neighboring combined linearly by weights. The optimal weights UEs reach an agreement [1]. While not as accurate that transform the inputs (channel parameters) as as infrared methods [3], a root mean square error close as possible to the target function (user po- of 2.5 m and 3 m was achieved in an indoor sition) are found in the offline training phase by Modern cell phones are equipped with a variety of sensors. UEs possess an inertial measurement unit (IMU), consisting of a gyroscope to measure rotation, an accelerometer to measure acceleration, and a magnetometer to measure the magnetic field intensity. Given the initial position of the user, the current user position may be obtained by integrat- ing the measured acceleration twice to get the user position. However, errors in IMU measurements grow with time - a constant offset in acceleration Fig. 3. Map generation on-the-fly and seeing through walls using measurement leads to a quadratic error in position. narrow beam antennas and multipath. Data from the sensors may be fused with channel measurement data using a Kalman filter/ extended Kalman filter (KF/EKF) to correct the drift in minimizing the closeness of the output of the neural IMU measurements. A KF is a recursive linear network to the target function at the reference estimator of the state (position and velocity) of points. a user. The current state of the user is modeled Machine-learning-based localization algorithms as a linear transformation of the state of the user require the availability of a dense fingerprinting at the previous time instant, based on kinematic database, the creation of which is a time-intensive equations derived from Newton’s laws of motion, process. The localization accuracy of fingerprint- whereas sensor measurements are modeled as a ing algorithms depends on the distance between linear transformation of the current state of the user. reference points, with the localization accuracy A KF is the optimal estimator of a linear process, typically on the order of the distance between given the mean and variance of the noise. If the the reference points. Additionally, changes in the relation is not linear, an EKF may be used to locally environment such as the addition of new furniture linearize the process via Taylor series expansion require the fingerprinting database to be re-created. [1]. The KF/EKF minimizes the mean square error Transfer learning may be leveraged to reduce the of the position estimate based on measurements amount of data required. Theoretical radio wave obtained from all sensors up to the current time propagation models are leveraged to replace data instant. When new information is obtained by the collection partially by ray tracing. The ray tracer, user in the form of new channel measurements or once calibrated to the environment based on the new sensor data, the KF/EKF recursively updates limited measurements conducted, may be used to the position estimate based on the old position predict channel parameters at the reference points. estimate and the new data. Minor changes to the propagation environment may be quickly incorporated into the environment map D. Localization Algorithms Exploiting Multipath utilized by the ray tracer, expediting the process of As discussed earlier, multipath components are creating (and updating) the fingerprinting database. conventionally thought to be a hindrance to ac- A neural network may be first trained on the curate localization. However, in conjunction with synthetic data generated by the ray tracer, with the a map of the environment, multipath components weights of the neural network refined by further provide additional vital useful information regard- training on real-world measurements. ing the location of the UE. For example, with a map of the environment available (Fig. 3), “forbidden C. User Tracking and Data Fusion transitions” of a UE wherein the UE moves through walls or from one floor to another in consecutive Localization accuracy of a stationary target may time steps may be detected and discarded. be improved by averaging the position estimate, Multipath components from the BS may arrive at reducing the variance of the estimate. For mobile the UE via a direct path or via indirect paths along targets, the location must be estimated in a shorter which the source ray suffers multiple reflections period of time, which can be achieved via user or scattering. Virtual anchors (VAs) are successive tracking. User tracking refers to continuously es- reflections of the BS on walls in the environment timating the position of a mobile UE, due to which [1], which are treated as an LoS BS in place of sudden changes in the user’s apparent position the physical NLoS BS. Future wireless devices from one sampling instant to another, caused by will exploit real-time ray tracing [10] for multipath positioning errors, may be smoothed out. propagation prediction in order to determine the TABLE I. Summary of the different position location techniques

Position Location Method Description BS Density Deployment Cost Accuracy Use uplink and downlink Fundamental Techniques AoA, ToA, TDoA measurements High Low [10] Low [10] to calculate position via geometry Use side-link (UE-UE) measurements to Cooperative Localization Low Low [1] Medium [1] complement BS-UE measurements Channel features mapped to Machine Learning Medium High [9] High [9] values stored in fingerprint database Refine position estimate User Tracking of fundamental techniques, Medium Low [1] Medium [1] predict user trajectory with sensor data Extract position information Multipath Exploiting Techniques Low Medium [7], [14] High [7], [14] embedded in multipath components

VA locations. If the user’s location is continuously scatterer and user locations where the expected dis- tracked with an EKF, each multipath component tances and angles (geometrically calculated) match received by the UE may be associated with a VA the measured distances and angles most closely in based on the previously estimated UE location. the least squared sense. Optimization techniques Once the correspondence between each multipath such as particle swarm optimization (PSO) and component and the VAs is known, any of the the LMA [14] may be used for nonlinear LS fundamental localization techniques (AoA, ToA, or optimization. TDoA) may be used to localize the UE. With large bandwidths and narrow beamwidths at IV. CONCLUSIONAND FUTURE RESEARCH mmWave frequencies, more multipath components This article has provided an overview of existing are resolvable, which makes the task of associating and emerging localization techniques, illustrating the multipath components with the VA more diffi- how utilizing the wide bandwidths at mmWave cult. Ray tracing may be used to take advantage of frequencies could lead to unprecedented localiza- NLoS multipath components arriving at a UE, pro- tion accuracies. The narrow antenna beamwidths viding single-shot user location estimation without at mmWave frequencies require smart beam man- user tracking. With knowledge of the AoA at the agement, while optimal localization requires an BS, the ToA of the source rays, and a map of the exploration of multipath components arriving from surrounding environment, the BS may determine all directions, for which a detailed study of joint the location of the UE via ray tracing each mul- communication and localization is required. Table tipath component. Since it is not known whether I provides a summary of the different position the signal is reflected or transmitted through each location methods. obstruction along the traced signal path, two possi- Looking into the future, we predict that a combi- ble locations are recursively stored as “candidate nation of machine learning, data fusion of measure- locations” at each obstruction encountered while ments from multiple sensors, and cooperative lo- ray tracing a multipath component. A majority of calization will be used for robust, accurate position the candidate locations will be clustered near the location. The wireless systems will need to seam- true UE location, so the user may be localized to the lessly transfer the localization responsibility from centroid of the largest cluster of candidate locations one wireless technology (e.g., WiFi access points [7]. indoors) to another (e.g., cellular BSs outdoors), In the absence of a map, with the assumption that similar to handovers in current cellular networks each multipath component is reflected or scattered when a user moves in and out of BS coverage cells. at most one time, the problem of determining With centimeter-level localization accuracy in the location of a UE can be reformulated into a future cellular networks, privacy will become a nonlinear LS estimation problem [14]. The scat- growing concern. Users must be allowed to opt out terer/reflector positions and the UE position and of tracking if they so desire, and any user location orientation are estimated by jointly finding the data stored in the network must be protected from hackers. Additionally, the localization solution must [5] T. S. Rappaport, J. H. Reed, and B. D. Woerner, “Position be robust to interference from malicious users, who location using wireless communications on highways of the future,” IEEE Communications Magazine, vol. 34, no. 10, Oct. could, for instance, attempt to replicate the refer- 1996, pp. 33–41. ence signals transmitted by the cellular network in [6] 3GPP, “E-UTRA; Requirements for support of radio resource order to gain unauthorized access to user location management (Release 15),” TS 36.133 V15.9.0, Jan. 2020. [7] T. S. Rappaport et al., “Wireless Communications and Appli- information. cations Above 100 GHz: Opportunities and Challenges for 6G The computing capabilities of UEs will enable and Beyond,” IEEE Access, vol. 7, June 2019, pp. 78 729– mapping and ray tracing in real time. We envisage 78 757. [8] C. A. Balanis, Antenna theory: analysis and design, 4th ed. that cell phones in the future shall generate a map Hoboken, New Jersey: John Wiley & Sons, 2016. of the environment on the fly or have maps loaded [9] J. Gante, G. Falc˜ao, and L. Sousa, “Deep Learning Architec- within, enabling map-based localization algorithms tures for Accurate Millimeter Wave Positioning in 5G,” Neural Processing Letters, vol. 51, no. 1, Aug. 2020, pp. 487–514. that exploit real-time multipath propagation. The [10] O. Kanhere and T. S. Rappaport, “Position Locationing for augmentation of human and computer vision will Millimeter Wave Systems,” in Proc. IEEE GLOBECOM 2018, allow users to see in the dark and see through Dec. 2018, pp. 1–6. [11] J. Xiong and K. Jamieson, “ArrayTrack: a fine-grained indoor walls [7]. Cell phones in the future will have the location system,” in USENIX, NSDI, Apr. 2013. capability to either download or generate a map [12] J. Schroeder, S. Galler, K. Kyamakya, and K. Jobmann, of the environment on the fly and “see in the dark” “NLOS detection algorithms for Ultra-Wideband localization,” in 2007 4th Workshop on Positioning, Navigation and Com- [7]. The UE will behave like a radar, measuring the munication, March 2007, pp. 159–166. distances of prominent features in the environment, [13] C. Huang et al., “Machine Learning-Enabled LOS /NLOS such as walls, doors, and other obstructions. Ad- Identification for MIMO Systems in Dynamic Environments,” in IEEE Transactions on Wireless Communications, vol. 19, ditionally, reflections and scattering off walls will no. 6, June 2020, pp. 3643-3657. enable cell phones to view objects around corners [14] A. Shahmansoori, G. E. Garcia, G. Destino, G. Seco-Granados, or behind walls [7], as illustrated in Fig. 3. and H. Wymeersch, “Position and Orientation Estimation Through Millimeter-Wave MIMO in 5G Systems,” IEEE For ranging measurements, a radar operates in Transactions on Wireless Communications, vol. 17, no. 3, the pulsed radar mode, wherein the radar transmits March 2018, pp. 1822–1835. a single pulse, switches from transmit to receive [15] J. Zhou, N. Reiskarimian, J. Diakonikolas, T. Dinc, T. Chen, G. Zussman, and H. Krishnaswamy, “Integrated full duplex mode, and waits for the echo back from the object radios,” IEEE Communications Magazine, vol. 55, no. 4, Apr. that is to be range-estimated. However, due to 2017, pp. 142–151. constraints on switching speed, only objects at a sufficient distance from the user may be ranged. BIOGRAPHIES For example, an mmWave phased array with a OJAS KANHERE received the B.Tech. and TX-RX switching time of ∼100 ns cannot range M.Tech. degrees in electrical engineering from IIT objects closer than 50 ft (electromagnetic waves Bombay, Mumbai, India, in 2017. He is currently travel 1 ft/ns). To range closer objects, a UE pursuing the Ph.D. degree in electrical engineering must simultaneously transmit and receive the radar with the NYU WIRELESS Research Center, New signal, operating in the full duplex mode, requiring York University (NYU) Tandon School of Engi- TX-RX isolation [15]. neering, Brooklyn, NY, USA, under the supervision of Prof. Rappaport. His research interests include V. ACKNOWLEDGMENTS mmWave localization and channel modeling. This work is supported by the NYU WIRELESS THEODORE S. RAPPAPORT (S’83-M’84-SM’91- Industrial Affiliates Program and National Science F’98) is the David Lee/Ernst Weber Professor at Foundation (NSF) Grants 1702967, 1731290, and New York University (NYU) and holds faculty 1909206. appointments in the Electrical and Computer En- gineering department, the Courant Computer Sci- REFERENCES ence department, and the NYU Langone School of Medicine. He founded NYU WIRELESS, a [1] T. Pedersen and B. H. Fleury, “Whitepaper on New Local- ization Methods for 5G Wireless Systems and the Internet-of- multidisciplinary research center, and the wireless Things,” COST Action CA15104, IRACON, Apr. 2018. research centers at UT Austin (WNCG) and Vir- [2] H. Guan, J. Li, S. Cao, and Y. Yu, “Use of mobile LiDAR in ginia Tech (MPRG). His research has provided road information inventory: A review,” International Journal fundamental knowledge of wireless channels used of Image and Data Fusion, vol. 7, no. 3, 2016, pp. 219–242. [3] C. D. McGillem and T. S. 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