Target Localization Utilizing the Success Rate in ... - IEEE Xplore

IEEE SENSORS JOURNAL, VOL. 6, NO. 5, OCTOBER 2006

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Target Localization Utilizing the Success Rate in Infrared Pattern Recognition Nikos Petrellis, Nikos Konofaos, Senior Member, IEEE, and George Ph. Alexiou, Member, IEEE

Abstract—The architecture of an indoor target localization system employing a small number of infrared-emitting diodes and sensors is presented in this paper. The properties of infrared light and magnetic fields have already been exploited for position localization in distances of several centimeters. Ultrasonic waves and laser light can be used for longer distance estimation if the system is capable of accurately measuring the time of flight of the reflected signals. The proposed approach intends to cover a distance of several meters without requiring high accuracy measurements and sensors of increased precision. The digital infrared patterns that are transmitted from a constant position are recognized by a pair of sensors mounted on the moving target, with varying success rate depending on the distance and the angular displacement from the transmitter. Processing the success rate instead of the analogue signal intensity requires low-cost digital microcontroller systems of moderate precision and computational power. Moreover, longer distances can be covered since attenuated, noisy, or scrambled patterns are also important for the position estimation in the proposed approach. A proper modeling of the pattern recognition success rate is presented in order to estimate distances of several meters with an adjustable estimation error. The use of multiple infrared pattern transmitting devices results in extension of the area covered and a reduction of the estimation error due to additional crosschecks that may be accomplished. The area covered can be increased by a factor between 20% and 100% depending on the allowed range overlapping of the transmitting devices. The potential topology of these devices is also discussed and analyzed. The presented system can be used in several virtual reality and robotics applications. Index Terms—Distance estimation, indoor navigation systems, IR sensor devices, pattern recognition modeling, sensor systems architecture.

I. I NTRODUCTION

T

HE INDOOR navigation system presented in this paper is based on measuring the quality of an infrared signal arriving at the active sensors of a moving target. This method was recently demonstrated to be suitable for target localization applications [1]. Passive instead of active infrared sensors have been used for the indoor navigation of moving vehicles like robots. One purpose for the use of passive sensors is to avoid obstacles located near the moving target [2]–[4]. Passive infrared sensors are often used in conjunction with ultrasonic transceivers and image processing devices [4]. In this case,

Manuscript received June 8, 2005; revised March 1, 2005. The associate editor coordinating the review of this paper and approving it for publication was Dr. Bahram Kermani. The authors are with the Computer Engineering and Informatics Department, University of Patras, 26500 Patras, Greece (e-mail: [email protected]; [email protected]; [email protected]). Digital Object Identifier 10.1109/JSEN.2006.881342

infrared sensors are responsible for the estimation of short distances, while longer distances can be estimated if the round trip time of the ultrasonic waves is accurately measured. Distance estimation can also be achieved by processing images captured from different angles by multiple cameras [2], [5]. However, the architectural and computational cost of such systems is not negligible at all. An interesting approach similar to image processing but with significantly lower cost is presented in [6], where the position of an object projected on a plane is estimated using only a few light emitters and receivers instead of a camera or a full array of light-sensitive sensors. A probabilistic approach has been proposed by Fox et al. [7], [8] for estimating the position of the moving vehicle. This technique is called “Markov localization” and is used in conjunction with active sensor handling for indoor navigation. The certainty of the moving vehicle on whether it is at a specific position is determined by both the history of the movements that the vehicle has already performed and the active sensing. Ultrasonic and laser-range finder are two types of sensors used in their work, and activating a specific sensor in the right direction is a dynamic process determined by the history of movements logged, the previous sensor values, and the direction the vehicle is heading at. Laser-range scanners are also used in [9] and [10] in order to extract three-dimensional (3-D) surface structure (optical depth) and to measure distances greater than 1 m between the laser transceiver and the object under consideration. Distance estimation is based on measuring the time of flight of the laser light and then applying the triangulation method. In a similar way, Aytac and Barshan [11], [12] employed infrared instead of laser light in order to extract the properties of the surfaces of objects existing several centimeters away from the sensors. The infrared light of constant intensity transmitted successively from a range of angles is reflected from the surface under test. The reflected light energy is measured, and conclusions about the type of the object surface as well as its distance can be extracted. Other similar approaches for measuring distances of less than 1 m using infrared light can be found in [13] and [14]. In order to accurately determine the position and alignment of machine tools in very short distances of the order of a few centimeters, the properties of magnetic fields can also be exploited, as shown in [15]. Angle measurement, rotation monitoring, surface profiling, and medical instrumentation handling are some of the applications that this approach is targeted for. A number of magnetic beacons emitting special codes are used in [16] in order to locate the position of a vehicle in a 10-m radius sphere.

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In this paper, we introduce a new approach to target localization. In our systems architecture, instead of having both the receiver and the transmitter of the infrared light placed onto the moving vehicle, we place one or more transmitters at specific positions at the edges of the region within which the vehicle wanders. This fact allows our navigation system to cover distances longer than 1 m due to low attenuation. Low-cost commercial infrared-emitting diodes and sensors are forced to communicate with a predictable packet loss instead of using beams of constant intensity and special purpose components with advanced sensitivity and angular displacement features. Although in the latter case accurate position estimations can be instantly provided, this approach has not been adopted for distances longer than 1 m since the infrared signal has been significantly attenuated and the small or noisy variations in intensity of the infrared signal from point to point cannot lead to valid coordinate estimations. In our case, the transmitting devices (IRTX) send specific digital patterns of infrared light over a carrier frequency in order to reject interference from other light sources. The receivers (IRRX) placed on the moving vehicle recognize these patterns with a success rate that depends on the distance and the angular displacement of the vehicle. The success rates of various types of infrared patterns define an identity for each position even if it is more than a meter away from the transmitting device. Although it cannot be reassured that all the positions provide different identities in order to distinguish from each other, the number of positions that may be confused can be reduced if more than one transmitting device is arranged properly. In the prototype system developed in our lab, a single transmitter can cover an area of approximately 5 m2 . This area can be extended if multiple transmitting devices are placed around the area under consideration. The coordinate estimation time depends on the desired position location accuracy and was carried out in less than half a second in our present set up. II. I NFRARED C OMPONENT F EATURES It is widely known that infrared-emitting diodes and photon sensors are affected by infrared light at the electron level. Thermal detectors are infrared sensors used in applications like focal plane arrays for night watch, fire fighting, etc. In the case of thermal detectors, the temperature of the sensor is altered by the infrared radiation [17]. Other applications of infrared light include remote thermometry [18] and gas detection [19]. The aforementioned applications are based on mid and long wavelengths (> 1.5 µm). Other components similar to those used in the system we examine in this paper have peak responses at 850–950 nm and are used in communication applications like remote controls and infrared data association (IrDA) applications [20]. In applications where the infrared light has to be strictly directional like in IrDA, then diodes with a narrow beam angle can be used (e.g., 20◦ ). On the other hand, if the infrared light has to be widely spread (e.g., for accessing a remote device from various distances and angular displacement), then diodes with a wider beam angle should be used. Several commercial diodes have a beam angle greater than 80◦ , and in this case,

the radiation energy has a lower intensity value due to a wider distribution. For example, diodes with an 80◦ beam angle have a radiant intensity of 3–6 mW/m2 , while the intensity of the narrower beam angle diodes exceeds the value of 20 mW/m2 (for a typical 200-mW power dissipation). The emitting diodes and phototransistors or photodiodes that can be used in our paper are usually fabricated by GaAs or GaAlAs technology. The most important issue affecting the stability of the presented target localization system is the interference from other infrared sources like sunlight and reflections. The interference from these other infrared sources is suitably handled by transmitting the patterns over a carrier frequency. The carrier frequency equal to 38 kHz is the typical one for commercial applications, and several IR receiving components exist having an embedded filter in order to reject the carrier signal with minimal external circuitry. Nevertheless, the target localization procedure can be accelerated if a higher carrier frequency is used. The highest carrier frequency that can be applied depends on the switching (rise and fall) times of the diodes. This parameter depends on the angle beam and the electrical current that a diode is driven by. Diodes with narrow angle beams (e.g., 12◦ ) have a minimum of 30 ns rise and fall time, while several microseconds may be needed for components with a wider angle. The prototype system developed in the laboratory for our case study employs infrared-emitting diodes with a moderate beam angle (40◦ ), while a commercial infrared sensor with an embedded 38-kHz filter has been used at the receiver end. The infrared sensor is supposed to cover a distance of at least 5 m if the horizontal and vertical angular displacement is 0◦ . For a ±30◦ horizontal and a ±15◦ vertical angular displacement, this minimum distance is reduced down to 3 m. III. P ROPOSED H ARDWARE S ET U P In the present approach, the target localization procedure is not based on measuring the intensity of a reflected infrared beam; hence, the radiation intense diagrams that are available in the infrared component datasheets are not directly used. As already mentioned, the IRTX device is transmitting various types of patterns that are received by the IRRX. The motive behind this concept is that following this way, different pattern types are recognized with different success rates. More specifically, the patterns having long period pulses can be recognized in longer distances than those having short period pulses. The steep of the curve representing the success rate according to the distance of the receiver from an IRTX device is not identical for different types of patterns. We exploit this fact in order to assign each position around the IRTX with a different set of success rate values. This set of success rates converges to specific polar coordinates, referring to the distance and the angular displacement of the target with respect to the IRTX device. Moreover, if pulse counting is used for pattern recognition, instead of measuring the light intensity with great accuracy, it requires the use of lower cost components for system construction and increases stability. The procedure can be described as follows: Various pattern sets are transmitted by the IRTX. Each set (referred from now

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Fig. 1.

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Architecture of the IRTX device.

on as a modulation pattern) contains a number of identical pattern codes, while two sets contain codes that differ in their structure in a way that will be explained in the forthcoming paragraphs. A constant number of codes of the same modulation pattern are transmitted from an IRTX device. Then, another set of codes with a different modulation pattern is sent. After transmitting codes with all the supported modulation patterns, a special preamble is sent, and the aforementioned steps are repeated. The angle and the distance of the receiver position differentiate the number of successfully recognized codes in each modulation pattern. The graphical representation of the average value of the recognized codes for each modulation pattern can be drawn as a function of either the angular displacement or the distance. The graphical representations of the success rates derived from experimental measurements can be stored as lookup tables, but this would require large-sized memories. For this reason, we approximate these graphical representations with nonlinear models. Consequently, a small number of equation parameters need to be stored instead of keeping all the individual success rate values in the memory of the processing unit. The success rate estimated from the average number of recognized codes for a specific modulation pattern may match several angle and distance pairs if the graphical representations are examined individually. Nevertheless, they are reduced to only one pair by crosschecking between different modulation patterns. What differentiates the various modulation patterns is the number of pulses in each code and the pulse period, because patterns consisting of long period pulses can travel error free in longer distances. Nevertheless, their reception rate is constantly successful in shorter distances. In order to be able to estimate the position of the target in this case, patterns consisting of shorter pulses are used. In the same way, it is more possible to achieve greater success rates in longer distances if the patterns consist of a small number of pulses. The architecture of the prototype IRTX device that has been implemented in the laboratory is shown in Fig. 1, where a power IR-emitting diode capable to transmit within a wide beam angle is used. In the case that emitting diodes with narrower beam angle are to be used, then some of them can be connected in parallel and placed in circular arrangement in order to cover

Fig. 2.

(a) Modulation pattern sequence. (b) Structure of a modulation pattern.

the desired range of angles in a uniform way. The pattern and carrier generator consists of a 38-kHz oscillator for the carrier signal and a circuitry able to generate a pulse train like the one presented in Fig. 2(a). This pulse train consists of a preamble indicating the beginning of a new round of modulation pattern transmission and a sequence of codes separated by pause intervals (P ). A number of M identical codes are transmitted from each modulation pattern MODn. The parameter n in the label MODn denotes the number of pulses in the specific modulation pattern. For example, the names MOD2, MOD5, MOD6, and MOD9 are describing codes with two, five, six, and nine pulses, respectively. The period of each pulse in a modulation pattern is determined by the parameters I and H, which represent the low and the high interval of a modulation pattern pulse, as depicted in Fig. 2(b). The period of a pulse in MODi is longer than the period of a pulse in MODj if i < j. It is obvious that MODi can travel error free for longer distances, while MODj is more suitable for use in short distances. This fact results in a successive increase of the difficulty in the pattern recognition for each MODi with increasing i. Fig. 2(b) shows this structure of the modulation pattern. A low-cost controller with some embedded flash memory can be used at the place of the pattern and carrier generator of the IRTX device. The pattern and carrier signals are mixed and amplified before driving the IR-emitting diode.

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Fig. 3. Carrier bandpass filtering and rejection.

The above describes the set up for one IRTX device architecture. On the other hand, if two or more IRTX devices are used, then the pattern recognition scheme should be modified. Thus, in this case, the IRTX devices transmit different modulation patterns since the receiver should be aware of the source of each pattern. For example, if two IRTX devices are used (ITRX1, ITRX2), MOD2, MOD5, MOD6, and MOD9 could be transmitted by the IRTX1, and MOD3, MOD4, MOD7, and MOD8 by IRTX2. This combination of modulation patterns for IRTX1 has been used in our lab in order to study how neighboring patterns like MOD5 and MOD6 behave in comparison with patterns that differ significantly like MOD6 and MOD9. Moreover, it is important to distinguish the case where code transmission from IRTX1 and IRTX2 overlaps or not. In the presented setup, we force IRTX1 to transmit a code when IRTX2 is in the pause P interval [Fig. 2(b)] and vice versa. If both IRTX1 and IRTX2 transmit pattern codes simultaneously, the nonlinear model that approximates the success rate behavior may be different than the one used here due to intensive scrambling. As already mentioned, the IRTX device transmits M times the same code from MODi before proceeding to the next MODj pattern. The parameter M has been set equal to ten for a tradeoff between acceptable precision and reasonable convergence speed. The receiver should be aware of the parameter M in order to estimate the success rate. The preamble has been implemented as a pause interval longer than P , but a special code different from any MODi can also be used instead. The IR sensor on the IRRX device filters the carrier and feeds the processing unit with the pure recognized modulation pattern. The bandpass filter and the carrier rejection circuitry can either be implemented externally if a custom frequency is used or be embedded into the IR sensor in case of a standard frequency like 38 kHz, a case depicted in Fig. 3. Moreover, if more than one IRRX device is installed onto the moving target, then all the sensors are connected to the same processing unit, as shown in Fig. 4. In our set up, the controller was serially connected to a host computer, which was assigned for the statistical processing of the recognized modulation pattern codes. If the microprocessor used can support multiplication and division in reasonable time and accuracy, the connection to the host computer can be omitted. The processing unit implements the pattern recognition operation based on a finite-state machine (FSM) like the one presented in Fig. 5. Heuristic rules are applied throughout the process, and the acceptance of a modulation code is context

Fig. 4.

Fig. 5.

Architecture of the IRRX device.

FSM for the IRRX processing unit.

sensitive, i.e., a modulation code may be rejected if detected in the wrong order because it has probably been derived by a scrambled pattern. The processing unit may follow additional heuristic rules in order to accept or reject a recognized modulation code. If SA = {MODA1 , MODA2 , . . . , MODAn } with A1 < A2 < · · · < An is the set of modulation codes an IRRX device can accept from IRTXA , and mAi is the number of occurrences of MODAi between two successive preambles, then the maximum value mAi can get is equal to M . The

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Fig. 6. Approximating the average values of the experimentally measured success rates using Richards model. TABLE I APPROXIMATION OF THE RICHARD’S MODEL PARAMETERS USING THE SIMPLEX OPTIMIZATION METHOD (500 STEPS)

current values of mAi are stored when a preamble arrives. After R rounds are complete (indicated by an equal number of preambles), the coordinate estimation can begin.

be stored. Moreover, solving the approximation equation is a faster procedure than searching a large lookup table. Richard’s sigmoid model [21] is a good approximation method for the presented setup and is described by the general equation

IV. C OORDINATE E STIMATION Before using the system in real time, a calibration procedure should be followed i.e., a number of measurements should be exploited in order to determine the success rate models that will be used during target localization. First, the average value of the success rate at various polar coordinates is measured. For example, Fig. 6 shows the experimental values of the success rate for MOD2, MOD5, and MOD9 measured at 15◦ angle in steps of s = 10 cm. Similar measurements have to be performed for all the supported modulation patterns and for all the supported angles in steps of ϕ degrees. For example, if an IRTX device is designed to cover the range [−90◦ , +90◦ ], and an angle accuracy of ϕ = 5◦ is adequate, then 18 diagrams similar to Fig. 6 have to be drawn in order to describe the behavior of the success rate in the range [0◦ , +90◦ ]. The range [−90◦ , 0◦ ] need not be treated separately because the success rate has a symmetrical behavior within these two ranges. The values retrieved in the previous step for a specific angle are approximated by a nonlinear function in order to estimate the expected success rate at the intermediate points that where omitted during the calibration phase. Approximating the experimental values by a nonlinear model reduces the memory requirements since only a small number of parameters need to

y=


a 1+

e−b(x−c)

 d1 .

(1)

The parameter y corresponds to the success rate, while x is the distance. The rest of the parameters (a, b, c, d) are estimated separately for each distance or angle step using the simplex method [22]. For the example in Fig. 6, the estimated (a, b, c, d) parameters are presented in Table I. As can be seen in Fig. 6, in short distances (< 1 m), the success rate has the maximum value (10/10), and no conclusion can be reached about the exact position of the vehicle in this range. An additional IRTX device would cover this area if placed at a proper position as will be explained in the following section. Between 1 and 2.9 m, the success rates are falling from 100% to 0% with different speeds for each modulation pattern, and this part is approximated by Richards’ model. A similar approach requires the experimental measurement of the success rates at various angles at a specific distance. For example, Fig. 7(a) and (b) shows the success rate related to the angular displacement at 2 and 2.5 m, respectively. In this figure, MOD6 has also been included in order to be able to compare the behavior of two neighboring modulation patterns (MOD5 and

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Fig. 7.

(a) Success rate related to angular displacement at 2 m. (b) Success rate related to angular displacement at 2.5 m.

MOD6). The success rate at a distance d is the same for two symmetrical angles +z and −z. Practically, a success rate measured individually may differ significantly from the average values plotted in Fig. 6 due to noise, reflections, and other environmental conditions. In order to increase the immunity of the system, a number of success rates are measured, and an initial averaging takes place. The success rates that deviate most from this initial average value are excluded, and a further and final averaging takes place using the rest of the measured values.

In order to demonstrate the way the graphical representations or the approximation equations can be exploited in order to estimate the moving target’s coordinates, we need to consider the values mv2 = 1.5, mv5 = 0.7, mv6 = 0.45, and mv9 = 0 measured for MOD2, MOD5, MOD6, and MOD9, respectively. If these values are used in Fig. 7(b), it is obvious that they converge to an angle of ±30◦ . On the contrary, if they are used in diagrams representing success rates for other distances, then the corresponding modulation pattern curves will not converge to the same angle, as shown in Fig. 7(a). Nevertheless, the

PETRELLIS et al.: TARGET LOCALIZATION UTILIZING SUCCESS RATE IN INFRARED PATTERN RECOGNITION

Fig. 8.

Loop for the coordinate estimation.

ambiguity of whether the target is +30◦ or −30◦ can only be clarified if a second IRTX device is used. The aforementioned coordinate estimation procedure can be described by the flowchart in Fig. 8. The parameter x in (1) has been expressed as ϕij , denoting the angle estimated if the approximation model corresponding to MODj and the distance i is used. The parameter y in (1) is the success rate sj measured for MODj. Finally, the parameter st denotes the distance for which the models like the one described by (1) have been constructed. V. R ESULTS AND A NALYSIS The major problems regarding the use of IR light are the existence of reflections and the interference from other infrared

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sources like sunlight. The latter is efficiently handled in our set up by transmitting the patterns over a carrier of specific frequency. In this way, only the valid signals are considered during position estimation, while infrared signals from irrelevant sources are automatically rejected. Although the effect of the reflected signal on the original one is tolerant and highly dependent on the real-time environmental conditions, it can be minimized by the following factors: 1) The use of two IRTX devices in several alternative topologies provides additional validation; 2) rejecting the samples with high deviation may result in noise canceling; and 3) system calibration. The calibration phase includes the determination of the a, b, c, and d parameters of the Richard’s model presented in (1) for various distance and angle displacement values in regular steps. If environmental conditions (e.g., object and person positioning, lighting, temperature, etc.) are significantly modified, the system controlling the IRRX devices can enter a new calibration phase. The limitations of our system caused by infrared signal reflections may be focused in two cases. 1) If room topology poses significant but stable reflections, the use of a nonlinear model other than Richard’s might be more proper in order to approximate the measured success rates during the calibration phase. For example, if more than one local minimum/maximum value appears in the experimental values, a polynomial function may be employed. 2) If the reflections are varying frequently due to person or object motion in the area, the calibration should be repeated more often. Moreover, the number of the success rate sample values encountered before a position is estimated should be increased in order to exclude those with higher deviation since they have probably been scrambled by unpredictable reflections. Of course, the potential cost in the aforementioned solutions is an increased convergence time and a reduced accuracy. The experimental results presented in this paper were retrieved in the center of a 5 × 7 m laboratory room under stable infrared light reflections, i.e., no objects or persons in the room were allowed to change position between successive measurements. Variation in the lighting and temperature conditions of the room did not affect the results since the 38-kHz carrier efficiently rejects any interference from other infrared light sources. The area that a single IRTX device can cover is shown in Fig. 9. The external curve (Rmax ) specifies the maximum distance an IRRX device can reach from IRTX according to its angular displacement. The maximum distance that could be measured in the lab was 3.1 m when the IRRX device faces toward the IRTX. As derived from Fig. 6, there is an area close to the IRTX device where no conclusion can be drawn about the exact position of the target. In this area, all of the modulation patterns arrive at the target with a 100% success rate. This area is specified by the internal curve (Rmin ) in Fig. 9. The Rmin and Rmax curves in Fig. 9 approximate the experimental results and can be described in polar coordinates in order to estimate the exact area that a single IRTX device can cover. Rmin is determined by the modulation pattern that fades closer to the IRTX (MOD9 in our setup), while Rmax is determined by the modulation pattern that can reach the longest distance

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Fig. 9. Area covered by a single IRTX device. TABLE II APPROXIMATED VALUES OF αi AND bi

(MOD2). Rmax and Rmin can be approximated by a fourthand eighth-degree polar function, respectively, i.e., Rmax (φ) = a0 + a1 φ + a2 φ2 + a3 φ3 + a4 φ4 , 0≤ϕ≤π (2) Rmin (φ) = b0 + b1 φ + b2 φ2 + b3 φ3 + b4 φ4 + b5 φ5 + b6 φ6 + b7 φ7 + b8 φ8 , 0 ≤ ϕ ≤ π. (3) The values of the parameters ai and bi that fit the experimental results are shown in Table II. The estimated area covered by an IRTX device in our set up is 5.61 m2 . It is well known that an equation like (4) calculates the area enclosed by two polar functions π A= 0

1 2 2 (R − Rmin ) dφ. 2 max

(4)

A second IRTX device placed at a proper position can improve the coordinate estimation procedure since it can cover the area surrounded by Rmin of the first IRTX device. Moreover, where the IRTX1 and IRTX2 ranges overlap, the coordinate estimation can take place with increased accuracy. A slightly different approach than the one described in Fig. 9 may take place in this case. The coordinate estimation procedure should not stop when reaching the first angle at which all of the modulation patterns of the IRTX device seem to converge. A small number of neighboring angles ϕi along with a result quality degree rki for each IRTXk device is estimated instead (e.g., r1i and r2i if two IRTX devices are used). Hence, the

selected angle (ϕi ) is the one having the largest value of the sum r1i + r2i . The IRTX2 device can be placed face to face with IRTX1, as shown in Fig. 10(a). Nevertheless, placing IRTX2 in an angle of around 150◦ from IRTX1 is preferable since this topology enables balancing of the quality of the values under test, as shown in Fig. 10(b). For example, the position P 1 falls within the limits of the angle that the IRTX2 device can cover, but it is almost in front of the IRTX1. On the contrary, P 1 is quite close to IRTX2 but not close to the IRTX1. Generally, when a position is difficult to be estimated by one single IRTX device, the second one should be used to provide more accurate estimations. The coordinate estimation time overhead depends primarily on the definition of the intervals (H, I, P ) for each modulation pattern. The minimum value that these parameters can get is determined by the carrier frequency. In our system, a 38-kHz carrier (26.3-µs period) was used, and the high pulse for each pattern was forced to last for at least five carrier periods in order to stabilize during the bandpass carrier filtering. The parameter H should last for at least 131.5 µs. The I parameter might be slightly shorter than the H one but should be higher than the carrier period. The pause interval P between successive patterns should be longer than I in order to distinguish the intervals between patterns and pulses. Selecting the values for these parameters to be equal to H = 135 µs, I = 50 µs, and P = 200 µs for the modulation pattern with the shortest period (e.g., MOD9), a single code will need less than 2 ms to be recognized. When two IRTX devices were used, the parameter P was further extended since the second IRTX

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15 ms if a similar analysis like the one described for the case of 38 kHz is applied. VI. C ONCLUSION

Fig. 10. Potential topologies of two IRTX devices. (a) 180◦ . (b) 150◦ . (Color version available online at http://ieeexplore.ieee.org.)

device was transmitting a valid code during this interval as already mentioned. If more than two IRTX devices are used in order to cover a larger area, there are two approaches that can be followed, namely 1) the transmission of codes derived from different IRTX devices should not overlap when the nonlinear approximation models presented here are to be followed, or 2) the code transmission may overlap but other proper models should be adopted. If the number M of successive identical patterns transmitted is equal to 10, then a single success rate value is estimated in less than 20 ms. The intervals defined above are increased for modulation patterns that are used for longer distances, such as MOD2, but the number of pulses is smaller in this case. Assuming that the estimation time overhead for a single success rate is the same in all modulation patterns and a number of five success rates is used for estimating the average values in the procedure depicted in Fig. 8, the position localization time overhead is then around 400 ms for a four-modulation pattern case (e.g., MOD2, MOD5, MOD6, and MOD9). In the procedure described above, the software processing overhead has not been clearly encountered, but this is highly dependent on the distance or angle steps that models like the one described by (1) have been a priori constructed. In a processing unit capable to perform multiplications and divisions using the system hardware, the software overhead is extremely lower than the 400 ms that have been estimated above. Using commercial infrared components with short switching times such as 30 ns (e.g., HIRL5010), the carrier frequency can be significantly increased. For example, using a carrier of 1 MHz, the coordinate estimation time overhead is reduced to

A position location approach is presented in this paper based on comparing the success rates of recognition among different modulation patterns. One or multiple infrared pattern transmitting devices can be used according to the desired accuracy or area that should be covered. The cost of the whole system is low due to the use of widely available commercial components. The area that could be covered by the prototype transmitter developed in the lab was more than 5 m2 . The position estimation error did not exceed ±10 cm, and the way that this estimation error can be further reduced was also discussed. The position estimation time overhead does not exceed 0.4 s if a carrier of 38 kHz is used. This time overhead can be significantly reduced if a higher carrier frequency is employed. The minimum and maximum distance the moving target can have from the infrared pattern source is presented in detail. Based on this distance range, a potential topology of two infrared transmitters was discussed and verified. The proposed solution can be used in cases where the distances under test are too short for laser scanners and ultrasonic wave radars [2]–[5], [9], [10] or too long for other approaches that also use infrared light [11]–[14]. The transmitter and receiver of the infrared light are not placed both on the moving target as in [11]–[14], and position estimation is based on recognizing different types of patterns instead of measuring the intensity of a reflected infrared beam. The vehicle can be a robot or a human user in virtual reality applications. Placing the multiple IRTX devices at proper positions is a critical point in order to minimize error and error tolerance in the estimation of vehicle location. Several virtual reality applications can benefit from the proposed target localization solution. In a virtual exploration of archaeological sites, museums, new buildings, etc., the position of the user has to be estimated in real time. Our navigation system can continuously provide the graphics software with the present user coordinates in order to determine the images that should be presented to him through the virtual reality glasses. Future work will focus on high-frequency carrier rejection architectures in order to achieve fast coordinate estimation. Using high-power emitters, more sensitive sensors and a proper combination of modulation patterns will increase the distances that can be covered by a single infrared transmitter. A detailed map showing the areas with high and low error estimation for the case of multiple transmitting devices in several topologies is to be constructed. In addition, techniques that take into consideration the movements history of the target, hence increasing the position estimation accuracy, will also be studied. R EFERENCES [1] N. Petrellis, N. Konofaos, and G. Alexiou, “Testing IR photon sensors for target localization applications,” in Proc. Int. Workshop Advances Sensors and Interfaces, Bari, Italy, Apr. 19, 2005, pp. 153–158. [2] R. P. Smurlo and H. R. Everett, “Intelligent sensor fusion for a mobile security robot,” Sensors, pp. 18–28, Jun. 1993.

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[3] D. Li, K. D. Wong, Y. H. Hu, and A. M. Sayeed, “Detection classification and tracking of targets,” IEEE Signal Process. Mag., vol. 19, no. 2, pp. 17–29, Mar. 2002. [4] J. Borenstein, B. Everett, and L. Feng, Navigating Mobile Robots: Systems and Techniques. Wellesley, MA: A.K. Peters Ltd., 1996. [5] A. Busboom and R. J. Schalkoff, “Direct surface parameter estimation using structured light: A predictor–corrector based approach,” Proc. Inst. Electr. Eng.—Vision, Image and Signal Processing, vol. 143, no. 2, pp. 109–117, Apr. 1996. [6] A. Coor-Harbo, “Geometrical modeling of a two-dimensional sensor array for determining spatial position of a passive object,” IEEE Sensors J., vol. 4, no. 5, pp. 627–642, Oct. 2004. [7] D. Fox, W. Burgard, and S. Thrun, “Markov localization for mobile robots in dynamic environments,” J. Artif. Intell. Res., vol. 11, pp. 391–427, 1999. [8] ——, “Active Markov localization for mobile robots,” Robot. Auton. Syst., vol. 25, no. 3/4, pp. 195–207, 1998. [9] J. A. Beraldin, “Integration of laser scanning and close-range photogrammetry—The last decade and beyond,” in Proc. 20th Congr. Int. Soc. Photogramm. and Remote Sensing, Commission VII, Istanbul, Turkey, Jul. 12–23, 2004, pp. 972–983. [10] M. Saiani, L. Gonzo, A. Simoni, M. Matteotti, A. Boni, and D. Petri, “Towards an FPGA-based implementation of 3-D laser-based scanner algorithms,” in Proc. Int. Workshop Adv. Sensors and Interfaces, Bari, Italy, Apr. 19, 2005, pp. 159–164. [11] T. Aytac and B. Barshan, “Simultaneous extraction of geometry and surface properties of targets using simple infrared sensors,” Opt. Eng., vol. 43, no. 10, pp. 2437–2447, Oct. 2004. [12] ——, “Position invariant surface recognition and localization using infrared sensors,” Opt. Eng., vol. 42, no. 12, pp. 3589–3594, Dec. 2003. [13] G. Benet, F. Blanes, J. E. Simo, and P. Perez, “Using infrared sensors for distance measurement in mobile robots,” Robot Auton. Syst., vol. 40, no. 4, pp. 255–266, Sep. 2002. [14] P. M. Novotny and N. J. Ferrier, “Using infrared sensors and the Phong illumination model to measure distances,” in Proc IEEE Int. Conf. Robot. Autom., Detroit, MI, 1999, pp. 1644–1649. [15] J. Kosel, H. Pfutzner, L. Mehnen, E. Kaniusas, T. Meydan, N. Vazquez, M. Rohn, A. M. Merlo, and B. Marquardt, “Noncontact detection of magnetoelastic position sensors,” Sens. Actuators A, Phys., vol. 123/124, pp. 349–353, 2005. [16] E. A. Prigge and J. P. How, “Signal architecture for distributed magnetic local positioning system,” IEEE Sensors J., vol. 4, no. 6, pp. 864–873, Dec. 2004. [17] A. Rogalski, “Infrared detectors: An overview,” Infrared Phys. Technol., vol. 43, no. 3–5, pp. 187–210, Jun. 2002. [18] J. Piotrowski and A. Rogalski, “New generation of infrared photodetectors,” Sens. Actuators A, Phys., vol. 67, no. 1–3, pp. 146–152, May 1998. [19] K. Yamashita, A. Murata, and M. Okuyama, “Miniaturized infrared sensor using silicon diaphragm based on Golay cell,” Sens. Actuators A, Phys., vol. 66, no. 1–3, pp. 29–32, Apr. 1998. [20] S. Williams, “IrDA: Past, present and future,” IEEE Pers. Commun., vol. 7, no. 1, pp. 11–19, Feb. 2000. [21] D. M. Bates and D. G. Watts, Nonlinear Regression Analysis and Its Applications, ser. Wiley Series in Probability and Statistics. Hoboken, NJ: Wiley, 1988. [22] M. Syslo, N. Deo, and J. Kowalik, Discrete Optimization Algorithms with Pascal Programs. Englewood Cliffs, NJ: Prentice-Hall, 1983.

IEEE SENSORS JOURNAL, VOL. 6, NO. 5, OCTOBER 2006

Nikos Petrellis received the diploma in computer engineering and the Ph.D. degree in electrical engineering from the University of Patras, Patras, Greece, in 1994 and 1999, respectively. From 1999 to 2003, he was a Senior Engineer with GiGA Hellas S.A. (an Intel company) and Atmel Hellas S.A. He currently teaches microprocessor and microcomputer courses at the Computer Engineering Department, University of Patras. His research interests include embedded system design for wireless automation and network applications.

Nikos Konofaos (M’98–SM’05) received the B.Sc. degree in physics from the University of Ioannina, Ioannina, Greece, in 1987, and the Ph.D. degree from the Department of Electrical and Electronic Engineering, University of Bradford, Bradford, U.K., in 1993. He spent two years as a Greek Army Officer and then was a Researcher and Consultant at various institutions in Greece. In 2000, he joined the Computer Engineering and Informatics Department, University of Patras, Patras, Greece, teaching and contacting research on microelectronics. He has more than 60 technical papers appearing in refereed journals and conference proceedings and is the author of two academic textbooks. Dr. Konofaos was a member of the technical committee of the IEEE International Symposium of Quality Electronic Design and is a Referee in various journals.

George Ph. Alexiou (M’93) received the B.Sc. degree in physics and the Ph.D. degree in electronics from the University of Patras, Patras, Greece, in 1976 and 1980, respectively. He is currently a Professor with the Department of Computer Engineering and Informatics and the Director of the Microelectronics (VLSI) Laboratory, University of Patras. He is publishing papers in a number of international journals and conference proceedings. His research interests include VLSI design, VLSI CAD tools, signal processing, digital systems, and RF data communications. Dr. Alexiou has served in all program committees of the IEEE International Symposium of Quality Electronic Design since 2000. He also has served in the program committee of IEEE Rapid System Prototyping Workshop since 2000.