Efficient Particle Filter Localization Algorithm in Dense ... - IEEE Xplore

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 61, NO. 10, OCTOBER 2014

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Efficient Particle Filter Localization Algorithm in Dense Passive RFID Tag Environment Po Yang, Member, IEEE, and Wenyan Wu

Abstract—The means of distributing dense passive radiofrequency identification (RFID) tags has been widely utilized for accurate indoor localization. However, they suffer a disadvantage on low localization precision due to the increasing interference of RFID tag collisions and the variation of behavior of tags. Current localization algorithms used in passive RFID location systems are mostly deterministic and have a limited capability on improving localization precision in a dynamic environment with uncertain sensor measurement. This paper investigates the feasibility of using particle filter technique as an efficient localization approach to deliver both relatively good accuracy and precision in dense passive RFID tag distribution applications. A position feature-based system model is first built to apply the typical particle filter technique in passive RFID location applications. Then, a new particle filter algorithm by using a moving direction estimation-based feature improvement scheme is proposed to enhance localization precision in a dense passive RFID tag environment. Experimental results show that the proposed method can provide relatively good accuracy and precision for passive RFID location applications, with an improved performance over the typical particle filter algorithm and a state-of-the-art deterministic method. Index Terms—Object localization, particle filter, radiofrequency identification (RFID).

I. I NTRODUCTION

R

ADIO-FREQUENCY identification (RFID) technology has been widely recognized as a cost-effective solution for indoor localization applications [1]–[5] in years. One fundamental goal of RFID location systems is to accurately and precisely localize the movement of the targeted object. Regarding achievable accuracy, the RFID location system can be classified in three categories: RF signal analysis-based location system [6], active RFID location system [7], [8], and passive RFID location system [9]–[14]. With a capability of feasibly deploying a dense tag distribution, the passive RFID location system normally can deliver accuracy up to 5–10 cm, which is relatively higher than that of the other two categories of RFID localization methods. Tesoriero et al. [9] created an RFID location system by employing a continuous grid of passive tag distribution with 4.3 × 4.3 cm on the floor. This system can reach localization accuracy up to 9 cm. Lee and Lee [10] proposed a passive RFID location system for mobile robots, Manuscript received May 3, 2013; revised July 24, 2013 and November 10, 2013; accepted November 27, 2013. Date of publication January 22, 2014; date of current version May 2, 2014. P. Yang is with the Department of Computer Science and Technology, Bedfordshire University, Luton, LU1 3JU, U.K. (e-mail: [email protected]). W. Wu is with the Faculty of Arts and Creative Technologies, University of Staffordshire, Stafford, ST18 0AD, U.K. Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TIE.2014.2301737

which deploys on 5 × 5 cm square tag pattern and uses a weighted average method to measure the positive of the object. This system can reduce accumulated errors and enhance accuracy up to 2.5 cm. Han et al. [11] presented a triangular based 5 × 5 cm passive RFID tag distribution pattern, which uses less tags and reduces localization accuracy error to 1.6 cm. Similarly, an RFID location system developed by Park and Hashimoto [12] is able to reach an accuracy of 13 cm on the x-axis and that of 5.7 cm on the y-axis by developing an efficient localization algorithm for mobile robots when using a 34 × 34 cm grid tag pattern. The aforementioned methods reflect that the utilization of dense passive RFID tag distribution is a typical means to achieve relatively high accuracy in the state-of-the-art RFID location systems. However, it also has some significant limitations. The first limitation is that the high density of tag distribution would increase RFID tag collisions [15]–[17] and further lead to low localization precision. The increase of RFID tag collisions is caused by the reduction of the distance and the greater variation of the behavior of tags. In this case, the precision of moving object localization is reduced, as shown in the aforementioned methods [9]–[12]. The second limitation is that most localization algorithms in passive RFID location systems are deterministic [9]–[12] and based on a perfectly constructed world model or observation. They have a limited capability to locate the object with uncertain sensor measurement in highly dynamic environments. Both these two limitations further lead to reduced localization precision in a dense passive RFID distribution environment. The efficient improvement of both accuracy and precision is a key challenging issue in current passive RFID location systems with dense tag distribution. For the past last decades, the particle filter is a well-known probabilistic localization technique [18], [19] for dealing with uncertain sensor measurement in highly dynamic environment. The possibility of utilizing probabilistic localization techniques [20]–[25] into RFID location systems has been investigated by many researchers. The first category of attempts [20]–[22] uses some probabilistic measurement models for implementing conventional particle filter algorithms [18], [19] to accurately localize and map RFID tags or readers. Relying on a relatively accurate environment map, these approaches can deliver stable localization performance. However, their hardware setup mostly requires integrating RFID devices with other laser range finders. Their localization accuracy up to the meter level usually inherits from the constraint of the typical RF signal analysis location system. The second category of methods [23]–[25] deals with stationary passive ultrahigh frequency (UHF) readers for localization. In this context, the moving object is attached

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 61, NO. 10, OCTOBER 2014

with one or more antennas to observe a 180◦ or 360◦ view of UHP tags in the environment. The position of the moving object is obtained by analyzing the received RF signal strength. However, the use of the UHF RFID reader easily causes false positive readings by environmental obstacles, so its localization precision is low. So far, there are no literatures illustrating an effective solution of using particle filter localization models in passive RFID tag environment for obtaining good accuracy and precision. This paper investigates the feasibility of using particle filter technique as an efficient localization approach to deliver good accuracy and precision in dense passive high frequency (HF) RFID tag environments. A four-position feature-based system model is first built to apply the typical particle filter technique [19] in dense passive RFID location environments. A new particle filter algorithm is proposed by using a moving direction estimation-based feature improvement scheme to enhance localization precision. This algorithm uses the position information-based straight observation model and 2-D Gaussian based motion model to predict and update the state of the localization system with the capability of reducing the ambiguities at each step. The proposed technique is validated by a number of experiments for proving its efficiency on improving the precision with a required accuracy. The experimental results show that the proposed method is capable of delivering higher localization precision for a given accuracy than the conventional particle filter algorithm FastSLAM [19] and a state-of-the art deterministic localization algorithm [11]. The major contributions of this paper are as follows. 1) A four-position feature-based system model is particularly designed to apply the typical particle filter technique in a dense passive HF RFID environment for achieving high accuracy. 2) A feature adjustment scheme is introduced to improve the correctness of features regarding the estimation of the moving direction of the object. 3) A new particle filter algorithm with a feature adjustment scheme is proposed, which is capable of improving localization precision for a given accuracy. The rest of this paper is organized as follows. Section II reviews some related work. Section III describes the construction system states and models. Sections IV and V present the proposed particle filter-based localization approach and experimental validation results, respectively. Section VI gives the conclusions and future work. II. R ELATED W ORK A. Dense Passive RFID Tag Distribution for Localization In a typical RFID location system with dense passive tag distribution, moving objects are attached to RFID readers, and multiple tags are distributed on the floor to locate objects. Each RFID tag marks a preliminary defined position point; the RFID reader observes a set of detectable RFID tags when the object moves to a new position. The localization algorithm is expected to enable the RFID reader to efficiently gather information and understand the context of the environment by using the RFID

Fig. 1.

Dense passive RFID tag distribution for localization.

tag’s location as well as stored information. Fig. 1 illustrates a moving object in a passive RFID location system. The RFID reader in Fig. 1 is assumed to read multiple passive RFID tags at each moving step. The position of the targeted moving object is denoted as O(x, y, z), which is equal to the position of the RFID reader. Z represents the height of the RFID reader, which is equal to the distance RH from an antenna plane of the RFID reader to a plane of RFID tag distribution. Q represents the number of tags detected by the RFID reader at each time interval t. The coordinates of passive RFID tags detected are denoted as (x1 , y1 ), (x2 , y2 ), . . . , (xm , yn ). At each time interval t, there is a spatial relationship between the position of the targeted object and these coordinates in Fig. 1, where fx and fy respectively represent localization algorithms to calculate the position of the targeted object from captured RFID data. From Fig. 1, it can be seen that this paper particularly focuses on the 2-D localization of an indoor moving object with a passive RFID localization system. Regarding the measurement of localization accuracy and precision, this paper uses the same definition as that in [31]: Accuracy is merely determined by tag distance denoted as Dx and Dy ; precision refers to accuracy errors. The measurement of a passive RFID location system includes the value, an error term, and the units, such as 10 cm +/−2.56 cm (i.e., accuracy is up to 10 cm and precision is within 2.56 cm). B. Probabilistic Localization and Particle Filter The probabilistic localization technique is originally derived from the simultaneous localization and mapping (SLAM) problem [26], [27], which refers that a mobile robot at an unknown location in an unknown environment incrementally builds a consistence map of the environment while simultaneously determining its location within this map. The theoretical framework of applying probabilistic localization technique in the SLAM problem relies on the Bayesian filter [26]. The popular implementation approaches for Bayesian filters are

YANG AND WU: PARTICLE FILTER LOCALIZATION ALGORITHM IN DENSE PASSIVE RFID TAG ENVIRONMENT

the extended Kalman filter (EKF) [29] and particle filter [18], [19]. EKF-based SLAM approaches are able to solve the linear localization problem but usually suffer from the data association problem [28]. In this case, the moving object fails to identify each feature practically, particularly when the mapping process is complicated. Another issue of an EKF-based SLAM approach is that its linear approximation of the motion and observation model can produce errors affecting map consistency [29]. In comparison to EKF, the particle filter [18], [19] based SLAM approaches approximate the posterior distribution by a random set of samples. This characteristic makes the particle filter have better robustness but higher computational complexity than EKF. This demerit is intractable for the highdimensional problem in SLAM. In order to overcome this drawback, Montemerlo et al. [19] proposed a state-of-the-art approach using the particle filter to solve the SLAM problem with a fast and accurate localization performance, called FastSLAM. In this paper, the proposed particle filter localization algorithm is based on a similar framework as this method.

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TABLE I C OMPARISON OF PASSIVE RFID L OCALIZATION AND SLAM

III. C ONSTRUCTION S YSTEM S TATE AND M ODELS Particle filter technique aims to solve the complex and dynamic SLAM problem by range-only sensors. However, the algorithm in the passive RFID location system (passive RFIDLoc) using dense tag distribution is a pure localization process by using absolute position sensors. This section compares the difference of the aforementioned two scenarios and constructs feature selection, system state, and models of the particle filter for passive RFID location systems. A. Comparison of SLAM and Passive RFID-Loc The primary difference between SLAM and passive RFIDLoc scenarios is on their sensor environments and type of features. In FastSLAM [19], sensors are range based and unfixed, so its features are the distance information at each time interval. In passive RFID-Loc systems, sensors are the positionbased fixed RFID tag and reader, and thus, the features are the position information stored in tags. Second, the impacts of features on the moving object position in the aforementioned two scenarios are dissimilar. In FastSLAM, distance-based features have impacts on both the x- and y-axes of the moving object position. In passive RFID-Loc systems, position-based features independently and separately influence the x- and y-axes of the moving object position. Third, the feasible application of FastSLAM is in an environment of unknown start position of the moving object and using range sensors. It contains feature estimation for both mapping and localization procedures. In the passive RFID-Loc system, although the observation of feature points contains some uncertainty and errors, the mapping between features and the moving object’s position is relatively consistent and explicit. It is a sole localization process without the mapping process. Finally, FastSLAM estimates the position of a moving object by using the observation model and the motion model. The procedure of the particle filter algorithm in FastSLAM contains initializing, applying model, weighting, and resampling. Due to the different nature of features, the par-

ticle filter localization algorithm in passive RFID-Loc systems needs to rebuild the observation model, the motion model, the definition of feature, and weighting and resampling process. Table I lists the differences between passive RFID-Loc and FastSLAM scenarios. B. Feature Selection As mentioned in Section II-A, in passive RFID location systems, features are extracted from the observed position information (x1 , y1 ), (x2 , y2 ), . . . , (xQ , yQ ) at each time step. Using a whole set of position information as features is the most straightforward method popularly adopted in many passive RFID location systems. However, the pure extraction of this position information leads to low precision because it is not fixed and symmetric. This paper compares three feature selection methods used in passive RFID location systems [10]– [12]. The criterion of feature section is based on the empirical observation of false-reading occurring probability in the effective RFID reader area of the RFID antenna. The findings show that the edge points of the effective RFID reader area have a much lower false-reading occurring probability than the middle points of the effective RFID reader area. Also, the distribution of false-reading occurring probability is not strictly symmetric and varies with moving trajectories. Therefore, the criterion of feature selection is to use the points with lower false-reading occurring probability and also to satisfy the symmetry. The first feature selection method is a traditional one, which contains the whole set of position information so it usually uses arithmetic mean or weight mean to get the targeted object’s position. The second and third methods treat feature as an area, so they mostly

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 61, NO. 10, OCTOBER 2014

TABLE II C OMPARISON OF F EATURE S ELECTION M ETHODS BY MAE

employ geometric mean to calculate a centroid of this area. Polygon area and rectangle area are used in the second and third methods. A number of experiments have been carried out to evaluate their localization performance. The experimental setup is in a real deployment, and all the experimental data are observed by real experiments. The RFID system used in the experiment contains one RightTag RFID Fixed Panel mid-range Reader with specifications as follows: operating frequency of 13.56 MHz, anticollision, antenna size of 66 × 30 cm2 , and multiple passive RFID button tags with HF. The antenna used in the RFID reader is a directional antenna. The antenna bandwidth is 1 MHz at −3 dB, and the antenna impedance is 50 Ω at 13.56 MHz. The localization accuracy is given as 10 cm, the same as the tag distance. During the experimental procedure, the RFID reader is attached on four wheels and moves forward step by step with given accuracy. In practical experimental processes, the distance of the RFID reader moving between two time steps is equal to the given accuracy (10 cm). The time intervals at each time step are given as 40 s for guaranteeing a sufficient number of tags detected. Two moving trajectories moving along the x- and y-axes are respectively tested. The time step of the localization sequence for each trajectory is measured by 12. Precision is measured by the mean absolute error (MAE) of localization accuracy, as illustrated in Table II. Table II shows that method 3 (rectangle area) gives higher localization precision than the other two methods. It may be because using rectangle area has better symmetry than using a polygon or point set. The features of the rectangle area can be denoted as F1 , F2 , F3 , F4 , which represent the right, left, top, and bottom borders of the rectangle area. At time interval t, if N tags T (xnt , ytn ) are detected by the RFID reader, the rectanglebased feature selection method is denoted in (1) and shown in Fig. 2   1 2 ⎧ 1 N F RightBorder = M ax x , x , . . . . . . , x ⎪ t t t t   1 2 ⎪ ⎨ 2 N Lef tBorder Ft = M in xt , xt , . . . . . . , xt  (1) 3 1 2 N T opBorder = M ax y , y , . . . . . . , y F ⎪ t t t t ⎪   1 2 ⎩ 4 BottomBorder. Ft = M in yt , yt , . . . . . . , ytN

C. System State and Models The system state contains the object location state of the presenting position of the moving object and the feature state of the presenting feature value. The feature state has been defined in the previous section. If the location state is defined as Ot in (2), where n is the index of feature points, t is the time interval. System state St can be denoted as

Fig. 2.

Impacts of feature state on location state in passive RFID localization.

Ot = (xt , yt )   St = Ot , Ft1 , Ft2 , Ft3 , Ft4

Location state

(2)

System state.

(3)

In order to implement the particle filter algorithm, two other models require defining, namely, the observation model and motion model. Their specific implementation is characterized by the nature of the sensor system and the motion kinematics. The observation model tells the probability of obtaining a mobile robot position at a certain location state. The Bayesian filter [26] in this case can be defined as a probabilistic distribution: P (Ot |Rt ), where Ot is the location state and Rt is the RFID reader readings over time t. Given a known feature selection method, this distribution can be replaced as P (Ot |Ft ), where Ft denotes the features in (1). In the passive RFID location system, features are relative position information and not distance or bearing information. A straight observation model can be built in ⎧ 1 R = Ft1 + δ ⎪ ⎨ t2 Rt = Ft2 + δ (4) 3 3 ⎪ ⎩ R t = Ft + δ 4 4 R t = Ft + δ where Ft1 , Ft2 , Ft3 , Ft4 are the observations of features at time interval t. δ is the noise of measurement at each time interval. Fig. 2 illustrates the impact of the feature state on the location state in the passive RFID localization approach at a time interval. The right and left borders Ft1 , Ft2 determine the xt position of the object location state; the top and bottom borders Ft3 , Ft4 determine the yt position of the object location state. A noticeable issue here is that the features are considered as simultaneously extracting from RFID raw data at a time step. The time interval in practice has to be sufficiently given to the RFID reader for observing RFID raw data. The motion model characterizes the location state Ot of the moving object over time. The main purpose of the motion model is to predict the next location state Ot+1 of the moving object given the most current one Ot . The implementation of the motion model depends on the characteristics of the motion kinematics of the moving object. In this paper, we assumed that the moving trajectory of the object is randomly associated with the moving direction or moving speed; then, we used a 2-D

YANG AND WU: PARTICLE FILTER LOCALIZATION ALGORITHM IN DENSE PASSIVE RFID TAG ENVIRONMENT

Gaussian model to approximate the motion. The advantage of the 2-D Gaussian motion model is that it is able to cover all possible motion directions. More specifically, when given the location state Ot at the time step t, to predict the location state Ot+1 at the time t + 1, a number of particles randomly from a 2-D Gaussian distribution with zero mean are drawn. These particles form a circle with the origin at Ot and the radius determined by the standard deviation of the 2-D Gaussian distribution. The standard deviation of this 2-D Gaussian is important for the predicting the accuracy of the location state. The typical working procedure of the particle filter algorithm in the passive RFID location system is as follows: The object starts moving from an initial position S0 with prior knowledge of the feature points—F01 , F02 , F03 , F04 . As the object keeps moving, the features at the next step would be predicted by the current statement k and updated. The value of the feature points would change as the object moves. IV. F EATURE I MPROVEMENT S CHEME In most of feature selection methods in passive RFID location systems [9]–[12], including our proposed one in Section III-B, the value of the features directly inherits from the position information of the extracted tag data without any modifications. As indicated in Section II-A, localization accuracy is known as equal to tag distance in a dense passive RFID tag distribution. In order to not exceed localization accuracy in practice, this paper considers a condition that the moving distance of the mobile object at each time interval is expected to be an integer multiple of accuracy. In this case, the known localization accuracy can be used as an adjustable factor to improve the correctness of feature. This section presents a feature improvement scheme by estimating the moving direction of the object to improve the correctness of the feature value with known localization accuracy. A. Rules of Moving Direction Estimation Ft1 ,

Ft2 ,

Ft3 ,

Ft4

Fig. 2 shows that four chosen features separately represent the border line of a rectangle-based RFID effective detection area over time. The width and length of this area can be defined as L and W . Theoretically, on a known environment (fixed sensing range and height of the RFID reader, constant moving speed, etc.), this rectangle area is expected to be approximately identical over time steps. The distance gap between Ft1 and Ft2 at each time step is supposed to be equal to L; the distance gap between Ft3 and Ft4 is supposed to be equal to W . Consequently, on a continuous moving trajectory over time step T , the features Ft1 , Ft2 , Ft3 , Ft4 have to follow these rules for keeping a uniformed moving direction. 1 1 1 , Ft1 = Ft−1 , or Ft1 < Ft−1 , then along the 1) If Ft1 > Ft−1 2 2 2 , or x-axis of object movement, Ft > Ft−1 , Ft2 = Ft−1 2 2 Ft < Ft−1 . 3 3 3 , Ft3 = Ft−1 , or Ft3 < Ft−1 , then along the 2) If Ft3 > Ft−1 4 4 4 y-axis of object movement, Ft > Ft−1 , Ft4 = Ft−1 , or 4 4 Ft < Ft−1 . 3) For each time step t, |Ft1 −Ft2 | = L, and |Ft3 −Ft4 | = W .

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To apply these rules, it would adapt the following actions. 1) Check if the features satisfy the rules. 2) Calculate the moving distances of four features of each object from the previous time step to the current time step. 3) Estimate the moving direction regarding the comparison of the current step and previous step of features. 4) Adjust the value of features by averaging the relative movement distance on the x- or y-axis of the object. B. Feature Improvement Scheme Regarding the aforementioned rules, the feature improvement scheme is introduced as follows. 1) Check if the features satisfy the rules. a) If the feature value follows the rules (1)–(3), no further actions are carried out. b) If the feature value follows the rules (1) and (2) but not the rule (3), it means that the moving direction of the object is clearly known while the feature value may not be accurate. The feature value is unchanged. c) If the feature value does not follow the rules (1) and (2), the actions (2)–(4) are carried out. 2) To calculate the moving distance of four features of each object    1,2,3,4  (5) Dt1,2,3,4 = Ft1,2,3,4 − Ft−1 . Actions (3) and (4) are carried out as follows. For the x-axis feature value, we have the following cases. 1 2 2 1 1) If Ft1 > Ft−1 and Ft2 = Ft−1 or Ft2 > Ft−1 and Ft1 = Ft−1 , the object possibly moves toward the x positive direction (right border in Fig. 2). 1 2 2 and Ft2 = Ft−1 or Ft2 < Ft−1 and Ft1 = 2) If Ft1 < Ft−1 1 Ft−1 , the object possibly moves toward the x negative direction (left border in Fig. 2).

For the aforementioned two cases, the feature values are adjusted as follows:   1 Ft1 = Ft−1 + Dt1 +Dt2 ×0.5,

  2 Ft2 = Ft−1 + Dt1 +Dt2 ×0.5.

In other cases, the moving direction is unsure, so feature values are unchanged. For the y-axis feature value, we have the following cases. 3 4 4 1) If Ft3 > Ft−1 and Ft4 = Ft−1 or Ft4 > Ft−1 and Ft3 = 3 Ft−1 , the object possibly moves toward the y positive direction (top border in Fig. 2). 3 4 4 and Ft4 = Ft−1 or Ft4 < Ft−1 and Ft3 = 2) If Ft3 < Ft−1 3 Ft−1 , the object possibly moves toward the y negative direction (bottom border in Fig. 2).

For the aforementioned two cases, the feature values are adjusted as follows:   3 Ft3 = Ft−1 + Dt3 +Dt3 ×0.5,

  4 Ft4 = Ft−1 + Dt4 +Dt4 ×0.5.

In other cases, the moving direction is unsure, so feature values are unchanged.

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Consequently, according to the theoretical model in the last section, the system state can be assumed as follows: ⎧ 1,2,3,4 WM,t =1 OM,t = (0, 0), ⎪ ⎪ ⎨ r = Accuracy ∗ α, α ∈ (1, 4) (7) 2 = near(−L/2) F 1 = near(L/2), FM,0 ⎪ ⎪ ⎩ M,0 3 4 FM,0 = near(W/2), FM,0 = near(−W/2) where Near() nearest detected tag in a border of effective RFID detection area; r radius of circle to apply the 2-D Gaussian motion model. B. Applying Models and Weighting

Fig. 3. Work flow of proposed particle filter algorithm.

V. A LGORITHM D ESCRIPTION This section represents the detailed work flow and description of the proposed particle filter algorithm. Corresponding to the feature model in (3), the weights of features are denoted as W 1,2,3,4 . The conventional particle filter structure only contains the localization state and feature states without including weight issues. However, the proposed algorithm puts the weight state into the particle state in

1,2,3,4 1,2,3,4 (6) , WM,t PM,t = OM,t , FM,t where M index of the particle; t time interval; OM,t location state of the moving object; 1,2,3,4 FM,t feature states of each particle; 1,2,3,4 weight states of each feature. WM,t The particle filter algorithm operates on a set of particles by iteration, which can be divided into three steps: 1) initialization (only in the first iteration); 2) applying models and weighting all the particles; and 3) resampling. The detailed work flow of the proposed particle filter algorithm is illustrated in Fig. 3. A. Initialization In FastSLAM [19], the start positions of the target and features are unknown, so the errors at the beginning stage are usually big. In passive RFID localization, the RFID sensor initially provides the position information, so the particle filter algorithm can start at a known position and feature points.

After initialization, the 2-D Gaussian motion model is applied to all particles. The location state of each particle will be replaced with a new predicted one generated from the motion model, and the predicted feature state of each particle will be updated as well. At time intervals t, once applying the motion model to (6), M particles get M numbers of different  , which are allocated in the predicted position states OM,t+1 circle of center point OM,t , as shown in step 2 in Fig. 5. A noticeable issue here is that we filter the predicted position state  by a square area with r because it is assumed that OM,t+1 the object only moves one integer time of accuracy. Given an approximately known RFID reader effective detection area, the predicted positions of features are estimated by (8) and shown in step 3 in Fig. 4 ⎧ 1,  ⎪ FM,t = OM,t + W/2 ⎪ ⎪  ⎪ 2, ⎨F  M,t = OM,t − W/2 (8) 3,  ⎪ = O + L/2 F ⎪ M,t M,t ⎪ ⎪ ⎩ 4,  − L/2. FM,t = OM,t Here, each particle has its estimation to the location state and feature states. The predicted location state is defined as the location state after the motion model is applied. Moreover, define the predicted observation as the feature value. Then, the weight of each particle is determined by the difference of the predicted observation in (8) and the real observation in (4). If the predicted location state and feature state are highly close to the real states, then the predicted observation is also highly close to the real observation. Hence, this particle will have a high weight. In a probabilistic math form, the weight of each particle is calculated using (9). Meanwhile, the four predicted feature points’ value is already got at time intervals t + 1, and the observation model by using the observed feature points’ data to calculate the weight of each particle can be applied ⎛ 2 ⎞ 1,2,3,4, 1,2,3,4 − R F 1 M,t+1 ⎟ ⎜ M,t+1 1,2,3,4 = (2πδ 2 ) 2 exp ⎝ WM,t+1 ⎠ . (9) 2 2δ The observation and weighting process is illustrated in step 4 in Fig. 4.

YANG AND WU: PARTICLE FILTER LOCALIZATION ALGORITHM IN DENSE PASSIVE RFID TAG ENVIRONMENT

continue running to estimate the moving object position   1, 2, 3, 4, Ft+1 Ft+1 + Ft+1 + Ft+1 , . Ot+1 = 2 2

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(13)

The flowchart and running samples of the proposed particle filter algorithm for the passive RFID localization system are shown in Fig. 4. VI. E XPERIMENTAL VALIDATION A number of experiments have been carried out, using different RFID data sets, different numbers of particles, and other various settings. The goal of these experiments is to evaluate the accuracy, robustness, and efficiency of this particle filterbased passive RFID localization solution and to investigate if this algorithm can be successfully implemented for indoor moving object position estimation and tracking. As for the comparison, an effective deterministic localization algorithm in the passive RFID localization approach [11] and FastSLAM [19] are implemented to process the RFID data sets. A. Experiments With Regular Data Sets Fig. 4.

Running sample of proposed particle filter algorithm.

C. Resampling In a typical resampling process [19], particles with large weight are duplicated while those with small weight are deleted. Also, all the weight accumulates to impact on the position value. However, the situation in this algorithm varies from the traditional one: The feature weight would separately influence the x- and y-axis values regarding the impacts of feature. Consequently, the first step is to find the largest weights in particles in   1,2,3,4 1,2,3,4 1,2,3,4 1,2,3,4 = M ax W , W , . . . , W WM 1,t+1 2,t+1 ax,t+1 M,t+1 . (10) Particles with satisfied weight are extracted by (11), as shown in step 5 in Fig. 4 Wm ≥ α ∗ WM ax

(11)

where Wm satisfied weight of particles; Wmax largest weight of particles; α filter parameter, usually from 0 to 1. Then, the predicted state of features can be accumulated by the particles with satisfied weight, as shown in M  

1,2,3,4, Ft+1 =

1

1,2,3,4 1,2,3,4, Wm,t+1 Fm,t+1 M  1



.

(12)

1,2,3,4 Wm,t+1

The updated position of moving object Ot+1 can be the next step moving object position state, and the algorithm would

The goal of these experiments is to evaluate the accuracy and precision of object localization with the proposed algorithm and traditional deterministic localization algorithm on different moving object trajectories. The radius of the circle for distributing particles is initialized as 30 cm at the beginning so that it can potentially cover all possible moving directions and positions. As for the start position, since every data set started from different positions, different ones are selected as start position. The number of particles would be assumed as 100 at the beginning. The experimental setup is similar to that in Section III-B in a real deployment. The RFID system used in the experiment is one RightTag RFID Fixed Panel mid-range Reader with specifications as follows: operating frequency of 13.56 MHz, anticollision, antenna size of 66 × 30 cm2 , and multiple passive RFID button tags with HF. The antenna used in the RFID reader is a directional antenna. The antenna bandwidth is 1 MHz at −3 dB, and the antenna impedance is 50 Ω at 13.56 MHz. For all validation in this section, the localization accuracy is given as 10 cm. During the experimental procedure, the RFID reader is attached on four wheels and moves forward step by step with the given accuracy. In practical experimental processes, the distance of the RFID reader moving between two time steps is equal to this accuracy, which is 10 cm. At each time step, the time intervals are 40 s. Three moving trajectories are respectively tested which are to move along the x-axis, along the y-axis, and along both the x- and y-axes. In each trajectory, the time step of the localization sequence is measured by 12. The experimental results of the object moving along the x-axis, y-axis, and both x- and y-axes are shown in Figs. 5–7. Fig. 5 shows the performance of both two localization algorithms on the situation that the moving object is only moving along the x-axis. The start position is (0, −2). Fig. 6 shows the performance of both two localization algorithms on the situation that

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TABLE III C OMPARISON OF P ROPOSED PARTICLE F ILTER L OCALIZATION A LGORITHM W ITH D ETERMINISTIC L OCALIZATION A LGORITHM [11] AND T YPICAL FAST SLAM [19] BY RMSE AND MAE

Fig. 5. Comparison of localization algorithms in Table III on x directional moving trajectory.

Fig. 6. Comparison of localization algorithms in Table III on y directional moving trajectory.

only moving along a random trajectory. The start position is (0, −2). Accuracy is already set as 10 cm; the precision values of different RFID tag distributions are compared by calculating the accuracy errors with different criteria, root-mean-square error (RMSE) and MAE, as shown in Table III. Table III shows that the proposed particle filter localization algorithm can work effectively on different trajectories in a dense passive RFID localization environment. Compared to the typical particle filter algorithm [19], it has significantly improved the localization precision from 8 to 4 cm. The main reason is because the resampling technique in FastSLAM and the quality of features are enhanced by the feature adjustment scheme in the proposed method. The redesigned system model assists the application of particle filter technique into a dense passive RFID localization system. The conventional particle filter techniques used in SLAM use the range sensorbased model, which generates even much lower localization precision. Compared to current state-of-the-art deterministic localization algorithms [11], the proposed particle filter localization algorithm has a slightly better localization performance. The localization precision values of all three trajectories have improved; for instance, RMSE reduces from 4.5 to 4.2 cm on the x trajectory, from 6.1 to 5.4 cm on the y trajectory, and from 3.9 to 3.5 cm on the random trajectory. Considering that the localization accuracy is already high, i.e., 10 cm, the enhancement of localization precision appears obvious. B. Computation Complexity

Fig. 7. Comparison of localization algorithms in Table III on a randomly directional moving trajectory.

the moving object is only moving along the y-axis. The start position is (0, −2). Fig. 7 shows the performance of both two localization algorithms on the situation that the moving object is

The number of particles used in this algorithm will significantly affect its performance. This section compares the localization performance of the proposed algorithm by using and not using the feature improvement scheme with different numbers of particles. Some experiments are carried out using increasing numbers of particles of 100, 200, 400, 600, 800,

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is large, the density of the distributing particle area is high, and the high probability of moving object position is covered; the probability of covering false-reading position is also increased. In our cases, as the number of particles increases to 1000, the RMSE of localization is not improved significantly. Therefore, the ideal number of particles in this case is approximately 1000. With this number, the particle can be distributed fairly to achieve good precision. C. Impact of Radius of Particle Distribution This section compares the performance of the proposed particle filter localization algorithm with different radii to distribute particles. The radius is normally at least larger than one integer time of accuracy since the moving object in this case continuously moves forward with a distance equal to accuracy. Few experiments are carried out using radius as 15, 20, 25, until 60 cm, respectively. Fig. 9 shows the performance of this algorithm by using and not using the feature improvement scheme in different radii of particle distribution. RMSE is used to reflect the localization precision. Similar to the last section, Fig. 9 shows that, with the use of the feature improvement scheme, the proposed algorithm can reach a better localization precision. It shows that, as the number of particles increases, the localization RMSE would initially decrease and then remain in a stable level or even increase a bit. The reason may be that the increasing radius of particle distribution at the beginning can produce a larger area to cover some highly possible features, which may be missed in a smaller radius. At the same time, the density of particle distribution is not affected significantly by the increase of radius. However, as the radius increases to some value, over 40 cm in Fig. 9, the density of particle distribution is significantly affected so that the RMSE of localization is not reduced slightly. The ideal radius in this case is approximately 30 cm, which is three times the localization accuracy. D. Discussion

Fig. 8. RMSE with increasing number of particles. (a) Algorithm with feature improvement scheme. (b) Algorithm without feature improvement scheme.

1000, and 2000, respectively. Fig. 8 shows the performance of this algorithm with different numbers of particles. RMSE is used to measure the localization precision. Fig. 8 shows that, with the use of the feature improvement scheme, the proposed algorithm can reach a better localization precision. As the number of particles increases gradually, the RMSE of the particle filter algorithm in the aforementioned two cases first decreases and then remains in a relatively stable level. However, occasionally, some outliners with larger RMSE occur when the number of particles increases. It is because the localization performance of the particle filter algorithm heavily depends on the number of particles being distributed. If the number of particles is small, the density of the distributing particle area is very low, so the high probability of position might not be covered. Oppositely, if the number of particles

Regarding the aforementioned results on precision with the given accuracy, it appears that the proposed particle filter algorithm with the feature improvement scheme has a superior performance than other localization algorithms. The traditional particle filter method in FastSLAM [19] has the highest accuracy error since its original framework is not suitable for the passive RFID localization scenario. The state-of-the-art deterministic method [11] can deliver advanced localization precision compared with the traditional FastSLAM method; however, it is lower than that of our proposed method. It implies that the proposed feature improvement scheme in the particle filter method plays a significant role in enhancing localization precision. Also, the selection of the number of particles and the radius of particle distribution in our proposed methods can impact its localization precision. Generally, the proposed method can provide relatively good accuracy and precision for passive RFID location applications, with an improved performance over other state-of-the-art methods [19], [11].

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mobile robot navigation environments. However, the main goal of this paper is to find out the possibility of applying the conventional particle filter algorithm as an approach to improve localization precision. This goal has been achieved by our proposed method on some scenarios. The limitations of this work can be possibly overcome by advanced RFID products and better algorithms in future. VII. C ONCLUSION AND F UTURE W ORK RFID-based localization has been a fundamental topic during the last decade as it allows the deployment of mobile systems within an unknown or partially unknown environment, particularly on passive RFID localization applications. In this paper, a particle filter algorithm is proposed to solve the passive RFID-based 2-D localization problem. This algorithm uses the position information-based straight observation model and 2-D Gaussian based motion model to predict and update the state of the localization system with the capability of reducing the ambiguities at the initialization step. Compared to the conventional deterministic localization algorithm in a passive RFID environment, the proposed algorithm can achieve approximately equivalent localization precision and accuracy, also with a better performance in highly dynamic environment. The efficiency of the passive RFID localization system can be improved by using the proposed particle filter algorithm. The strengths and limitations that arise throughout the progress of this work will lead to the following issues that warrant future research. First, the current implementation of the motion model only makes use of the previous location state and totally ignores the past states. Possible improvements can be made to consider the historical location states so that we can obtain some directional information. Second, the time interval and object moving trajectory are specified by RFID infrastructure. It would be interesting if this algorithm with a better RFID device can work in real autonomous mobile robot navigation environments. The two directions would be considered in further research. R EFERENCES Fig. 9. RMSE with increasing radius of particle distribution. (a) Algorithm with feature improvement scheme. (b) Algorithm without feature improvement scheme.

There are also some shortcomings in our proposed method. The gained precision in this paper is based on some specific experimental assumptions and optimized parameters. First of all, the time interval and object moving trajectory are specified by RFID infrastructure. Regarding the findings in [31], localization accuracy is determined by tag distance. For the known RFID device, the time interval and tag distance are justified by achieving the best system reading efficiency of the RFID infrastructure. Second, the number of particles and radius of particle distribution in the algorithm have to be evaluated for the given localization accuracy. The localization precision only can be improved by the proposed algorithm while any movements of the object follow these aforementioned assumptions. These may limit the applicability of this method in real autonomous

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Po Yang (M’14) received the B.Sc. degree in computer science from Wuhan University, Wuhan, China, in 2004; the M.Sc. degree in computer science from Bristol University, Bristol, U.K., in 2006; and the Ph.D. degree in electronic engineering from the University of Staffordshire, Stafford, U.K., in 2011. He is currently a Postdoctoral Research Fellow with the Department of Computing, Bedfordshire University, Luton, U.K. Before he joined Bedfordshire University, he was a Research Assistant with the University of Salford, Salford, U.K. His main research interests include radio-frequency identification and sensor networking, document image processing, computer vision, GPU, and parallel computing.

Wenyan Wu received the B.Sc. and M.Sc. degrees in electronic engineering and computer-aided design from the Dalian University of Technology, Dalian, China. She received the Ph.D. degree in water quality modeling and optimization in water distribution system from the Harbin Institute of Technology, Harbin, China, in 1999, and received the second Ph.D. degree in three-dimensional visualization and virtual reality from the University of Derby, Derby, U.K., in 2003. She is currently a Professor in Digital Design and Technologies at the University of Staffordshire, Stafford, U.K. Before she joined the University of Staffordshire, she was a Senior Lecturer in the Department of Computing at the Harbin Institute of Technology, and a Research Fellow in Water Software Systems at De Montfort University, Leicester, U.K. Her research interests include digital design, smart sensors and sensor networks, intelligent monitoring, modeling and optimization, water distribution systems and water resource management, simulation and data visualization, digital processing, and advanced interface.