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This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).

Recently, Impulse Radio Ultra Wideband (IR-UWB) signaling has become popular for providing precise location accuracy for mobile and wireless sensor node localization in the indoor environment due to its large bandwidth and high time resolution while providing ultra-high transmission capacity. However, the Non-line-of-sight (NLOS) error mitigation has considerable importance in localization of wireless nodes. In order to mitigate NLOS errors in indoor localization this paper proposes and investigates a novel approach which creates a hybrid combination of channel impulse response (CIR)-based fingerprinting (FP) positioning and an iterative Time of Arrival (TOA) real time positioning method using Ultra Wideband (UWB) signaling. Besides, to reduce the calculation complexities in FP method, this paper also introduces a unique idea for the arrangement of reference nodes (or tags) to create a fingerprinting database. The simulation results confirm that the proposed hybrid method yields better positioning accuracies and is much more robust in NLOS error mitigation than TOA only and FP only and a conventional iterative positioning method.

Lately, the Impulse Radio (IR)-Ultra Wideband (UWB) has been applied for the wireless personal area network (PAN), body area network (BAN), and RFID standards IEEE802.15.4a, 802.15.6, and 802.15.4f, respectively, because IR-UWB can perform ultra-high resolution of ranging, positioning, and high speed data transmission. Among the variety of prospective applications of IR-UWB, one of the most promising is in wireless sensor networks (WSNs), which require both robust communications and high-precision ranging capabilities [

Since IR-UWB signals have very short duration pulses, they can provide very accurate ranging and positioning capability in short range indoor radio propagation environments. Besides, the high time resolution characteristic of the UWB signal makes the time of arrival (TOA) method a good choice for location estimation in UWB communications. However, UWB localization and communication have to face lots of challenges like signal acquisition, multipath effects, multi-user interference and NLOS propagation. Among them NLOS propagation introduces positive bias for ranging estimation. Hence, it is difficult to recognize the impact of NLOS conditions on localization systems and to develop techniques that mitigate their effects [

Although, with enhanced sampling rates UWB receivers can accomplish high TOA resolution, their operating ranges are typically limited in reduced interference [

Besides the FP method, to achieve higher accuracy in localization of mobile nodes, many conventional iterative methods like Gauss-Newton, Steepest Descent, Levenberg-Marquardt have been proposed in conjunction with TOA or TDOA methods for mitigating NLOS errors. Among them, the Steepest Descent algorithm shows the slowest convergence in the final iteration steps, but for maintaining higher accuracy and low complexity the Levenberg-Marquardt method is the most suitable one to estimate a node location among the iterative methods [

In our proposal, we introduce a hybrid positioning technique which combines a Channel Impulse Response (CIR)-based FP method with polygonal arrangement (which will be discussed in a later section) of reference nodes (or tags) and an iterative-TOA positioning method using UWB signaling for wireless

In this paper, we would like to concentrate on the successful implementations of our proposed hybrid method in an indoor radio propagation environment such as a typical office room. We shall discuss on how to utilize the positioning results of the CIR-based FP method with different polygonal arrangements (which will be discussed in Section 3) of reference nodes in an iterative-TOA positioning method and to get the mutual benefits of the TOA and FP methods in a hybrid method. Besides, we would like to limit our discussion on the positioning performances of FP and proposed hybrid localization considering various NLOS incidences experienced by access points (AP)s. We also compare the performances of proposed hybrid method with TOA only, FP only and a conventional iterative-TOA method in LOS cases as well as worst cases of NLOS. Besides, the detailed mathematical analysis of computational complexity of the proposed hybrid method is beyond the scope of this paper and hopefully would be provided in a future issue.

The remaining of the paper is organized as follows: Section 2 illustrates the preparations for our proposal for hybrid positioning algorithm. The detailed analysis for our proposal for hybrid algorithm is provided in Section 3. Section 4 summarizes the model's performance using computer simulations. Finally, a brief conclusion is made in Section 5.

Before introducing our hybrid positioning algorithm the following three subsections are the necessary preparations for our proposal. Section 2.1 discusses and proposes a deterministic channel model constructed by a Ray Tracing technique for indoor radio wave propagation. Then, the CIR-based FP positioning method for an indoor propagation environment is explained in Section 2.2. Section 2.3 provides an overview on UWB TOA-based accurate ranging in a multipath environment.

We consider a typical well-furnished office room that provides an efficient means of simulating a short range indoor UWB channel. Due to reflection, transmission, refraction, and diffraction by obstacles signal propagation suffers from severe multipath effects in an indoor environment [

A general model of band-limited complex channel impulse response (CIR) is expressed as [_{n}_{n}_{n}

We employed the RT technique to identify the components of the above mentioned channel model. RT technique follows the ray launching approach (based on geometrical optics), which involves a number of rays launched uniformly in space around the transmitter antenna (

The complex electric field incident at the receiver due to the

_{0}: transmitted electric field

_{c}

_{n}

_{n}: number of reflections of

_{n}: number of penetrations of

Γ_{r}: coefficient of

P_{q}: coefficient of

In _{n}

The estimated CIRs described in the previous subsection are utilized for FP positioning and TOA based ranging (will be described in Section 2.3). The contents of Sections 2.2 and 2.3 are the preparations for the hybrid algorithm described in Section 3.

The CIR-based FP positioning, is also known as pattern matching technique, is especially suited for positioning under dense multipath conditions [

In the training phase, location signatures (fingerprints) based on the received signal's CIRs' are measured by several access points (APs) for various reference locations of nodes in a region to construct a fingerprinting database.

In the positioning phase, the run time estimation of node's location is performed by correlating the CIRs' of received signal with the fingerprints stored in the database.

Generally, the FP technique requires exhaustive calculations for fingerprinting pattern matching as well as creating a database for storing the location fingerprints (signatures). Therefore, the computational complexity in the FP technique depends greatly on the size of the fingerprinting database employed in pattern matching. As the fingerprints of the reference nodes constitute a database, the larger the number of reference nodes the larger the size of the fingerprinting database needed to cover the whole area, but employing a smaller number of reference nodes reduces the computational complexity, although it will not provide a good accuracy in positioning. In order to combat this positioning constraint we introduce a new idea to optimize the number of reference nodes to cover the whole area as well as to generate a fingerprinting database of optimum size.

In our new idea, we arrange the reference nodes in the room to form polygon shapes (as shown in

In the training phase of the FP method, signatures (fingerprints) h(l,t) are estimated and recorded for various reference locations I ∈ R, where R is the region of interest. Location of a node (or, tag) in 2D horizontal plane is defined as l(x,y) in Cartesian coordinates.

In the positioning phase, an estimate _{0} of the true instantaneous position _{0}, is obtained by using the corresponding instantaneous estimated CIR, _{0},

For, _{0},

Due to imperfect channel estimation at the receiver the ideal correlation cannot be attained. Hence, a correlation threshold _{th}_{th}_{th}

The TOA estimation technique used with UWB transmission can be used for conducting accurate ranging in indoor multipath environments. For the indoor multipath channel, the impulse response is usually defined as [_{p}_{k}_{k}_{k}

For TOA estimation, we employ a threshold-based first path detection method which utilizes the strongest path of CIR. This method makes use of an iterative search back algorithm, referred in [

The deterministic channel model described in Section 2.1, the FP positioning method explained in Section 2.2, and followed by UWB TOA-based ranging technique described in the Section 2.3, are the preparation for the hybrid localization that will be described in this section. Our proposal for hybrid localization is a combination of the CIR-based FP positioning and iterative-TOA positioning methods.

It is obvious that an iterative positioning algorithm needs an initial guess which will influence the iterative process. A good approximation of the initial value also reduces the calculation complexity as well as increases the positioning accuracies. In our proposed method, we initialize the iterative-TOA positioning with position location estimated by the CIR-based precalculated FP method. As the CIR- based FP method has better positioning accuracy than the TOA method in dense multipath indoor conditions, the location point estimated in of the FP method acts as a better initial value for our hybrid method. Moreover, by varying the size of the fingerprinting polygons (as discussed in the previous section), the positioning errors as well the complexities in the proposed hybrid method could be minimized. The brief idea of the proposed localization algorithm is provided in the flow chart in

In hybrid positioning, the node location estimated in the FP method is considered as the accurate initial values for an iterative-TOA method. The Levenberg-Marquardt based iterative algorithm (will be described in the later part of this section) is used to mitigate NLOS error in the iterative process of the hybrid positioning.

Let _{i}_{i}_{0}_{0}_{i}

Let us define the range between position (_{i}

As _{i}_{i}

If (_{es}_{es}_{i}

In _{0} = [_{0}, _{0}]^{T} be the position vector and _{0}) = [_{1}(_{0}), _{2}(_{0}),…, _{M}(_{0})]^{T} be the distance vectors. According to _{0}) can be written as:
_{es}

Thus, the most

Then, according to [

The minimization of tshe cost function is shown as:

Then, we assemble the individual components of [_{i} _{i}(^{n}→R^{m} defined by:

Using the notation ‖. ‖_{2} for Euclidean norm, we can rewrite

Consider the linear case, where every _{i}(

Setting Φ(_{min} = − (^{T} ^{−1} ^{T}

In order to estimate the node position by reducing NLOS error, it is required to minimize the cost function Φ(

The Levenberg-Marquardt algorithm starts with an initial value, ^{(0)} = [x^{(0)},y^{(0)}]^{T} which is obtained from the FP method. In ^{0}^{0}^{T} J^{0}

Here, ^{n}^{+}^{1}^{n}^{+}^{1}

This section provides the performances of our proposed hybrid method for node positioning by computer simulations. The performances of FP positioning method with various fingerprinting polygons (discussed in Section 2.2) are also evaluated in this section. Moreover, a comparison of the proposed hybrid method with TOA only, FP only and a conventional iterative method is shown in the later part of the section.

To get the accurate results in the ray launching type RT technique, many rays have to be launched and only a fraction of these reach the receiver. The accuracy of this technique also depends on the radius of the reception sphere. If it is too small, rays will pass by. If it is too large, paths might be duplicated. We set the radius of the reception sphere at 1.5 cm. Moreover, we consider the reflection of rays up to the third order, since the received signal strength beyond third order ray reflection becomes very low and is neglected. CIRs collected by the RT technique are convoluted with a UWB signal with bandwidth of 2 GHz. To consider a sufficient amount of multipath effects in the CIR pattern for NLOS incidences we allow a wider observation window,

As we consider an indoor office room of size 8 m × 6 m for our simulations, we think four APs are enough to cover radio mapping of this limited area and adequate for CIR-based FP positioning. It is obvious that increasing the number of AP would increase the possibility of LOS incidences and thus improve the accuracy in TOA-based positioning. However, increasing the number of APs will also increase the size of the fingerprinting database and thus increase the computational complexity of positioning in the FP method.

To evaluate the performance of the FP method for positioning of the same test nodes which are used in an iterative TOA positioning, we place the reference nodes (or tags) in such a manner to form polygon shapes (as mentioned in Section 2.2). We employ three different sizes of polygon, 20 cm, 30 cm and 50 cm and evaluate the positioning accuracy by varying signal's sampling rate (

In NLOS situations, unlike LOS conditions the position estimation mostly depends on the multipath components of the received signal. Therefore, the estimated position would be very far away from the real position of the test node in NLOS conditions. As the estimated position is shifted from its real position, it is more likely to match the CIR pattern of estimated location with that of a far location point rather than matching CIR pattern of the point near to its real position. Figures (7) and (8) show the CDF of distance error in the FP method for worst cases of NLOS. With a NLOS rate of 0.5, the case with polygon size of 50 cm (with sampling rate 50 GHz.) has a 35% probability to achieve accuracy of less than 1.5 m, whereas the cases with polygon sizes of 30 cm and 20 cm have a probability of 20% to achieve accuracy of less than 1.5 m. Therefore, the case with polygon size of 50 cm (with sampling rate 50 GHz.) performs slightly better than other cases with different polygons because when the NLOS rate is 0.5, two of the APs cannot see the test node and CIR patterns measured by these NLOS APs are also considered for node positioning. These two NLOS CIR patterns of the test node could have a better match with the CIR patterns of reference node points of a polygon size of 50 cm (which are relatively far away from the real position of the test node) rather than matching with the CIR patterns of reference node points of polygon size 30 cm and 20 cm. Similarly, when the NLOS rate is 0.75, three out of four APs cannot see the test node and CIR patterns measured by these three NLOS APs are also considered for node positioning. Therefore, in these worst NLOS conditions, for the reasons stated above, the case with polygon size of 50 cm performs slightly better than other cases.

Besides, in the above results, the cases with higher received signal sampling rate sometimes provides slightly better positioning accuracies. Because, the higher sampling rate of the received signal reduces the time quantization error in the time delay estimation. Therefore, positioning accuracy increases with higher time resolutions.

It is obvious that, the smaller the polygon size the larger the fingerprinting database as well as the higher calculation complexity will be in the FP method. Alternatively, the cases with the larger polygon size provide lesser calculation complexity in FP positioning.

In the proposed hybrid method, there is possibility to get mutual benefits from the TOA only and FP only methods. If any case in FP method does not have better positioning accuracy than the TOA only method, so the hybrid method can choose its initial value from the positioning result of TOA only method for iterations.

The positioning accuracies for different cases using the proposed hybrid method can be increased by increasing the iteration number. However, as depicted in

We have proposed a hybrid approach for localization of mobile or wireless sensor nodes using UWB signaling which combines the CIR-based FP positioning and iterative-TOA real time positioning methods. The proposed hybrid method follows a Levenberg-Marquardt based iterative algorithm, which effectively reduces NLOS errors. Our simulation results show that the proposed hybrid method yields better performances and is more robust in NLOS error mitigation than TOA only, FP only and a conventional iterative method. Moreover, the smaller size of polygons, resembling reference nodes (or tags) arrangement in the FP method, the bigger the size of fingerprinting database as well as the higher calculation complexity in positioning. Alternately, the larger size of polygon provides a smaller fingerprinting database compromising positioning accuracies. The optimum size of polygon could be predicted while evaluating the trade-off between accuracies in the proposed hybrid method and the complexities in positioning calculations, which is beyond the scope of this paper. A detailed analysis of the computational complexity of the proposed method will be provided in our next paper on this topic.

Ray tracing for indoor propagation modeling.

Size of polygon.

Arrangements of reference nodes in the room forming polygonal shapes.

Search-back detection method.

Flow chart for the proposed positioning algorithm.

CDF of the distance errors for FP method in different cases with LOS.

CDF of the distance errors for FP method in different cases with NLOS rate = 0.5.

CDF of the distance errors for FP method (8

Positioning accuracies in hybrid method for various cases with polygon sizes in LOS and different NLOS conditions.

Performance of the hybrid method in each iteration for different NLOS conditions, considering fingerprinting polygon size: 20 cm and signal sampling rate: 50 GHz.

Comparison on positioning accuracies for TOA only, FP only, conventional iterative TOA and hybrid methods in NLOS conditions.

Parameter values for evaluating model's performance.

Transmitted signal | Gaussian monopulse |

Channel bandwidth | 2 GHz |

Sampling frequencies | 30, 50, 80 GHz. |

AWGN parameter | σ = 0.5 m |

Transmitted Power | −60 dBm |

Fingerprinting polygon size | 20 cm, 30 cm, 50 cm |

Number of Access Point | 4 |

Area | 8 m × 6 m |

Properties of the materials employed in RT technique.

_{r} |
|||
---|---|---|---|

Concrete wall | 0.01 | 9 | 7.5 |

Table, chair, cabinet (wood) | 10^{−5} |
13 | 3.0 |

Window (glass) | 10^{−12} |
7.6 | 3.0 |