A design study on complexity reduced multipath mitigation

Summary: The driver sometimes is not satisfied by the route chosen by the Car Navigation System. Therefore, the function and the concept of the current Car Navigation System were examined. It can be said that the driver's subjectivity can be changed based on objective conditions. There are three main results. 1. Derive the objective element, factoring in predicted conditions. 2. Based on a conditional node, make predictions which will make a judgment about the subjective element. 3. Example all possible route and at the conditional node make a judgment about the subjective element based on the weight of all the conditions. The background of the research was obtained from a practical example of decision-making problems. It is a system rapidly spreading throughout Europe, America and Japan. The system can be described a functional part of the car, guiding the driver to the desired destination by the displaying road information. This is called a Car Navigation System in Japan. When this is used, the drivers depend on the map information shown on the display. Based on the input given by the driver, the Car Navigation System shows the most efficient route based on time and distance. Generally, drivers do not drive the most direct route to the destination point. Usually when driving, even if a person preplans the route, he will have a desire to make impulsive stops along the way. Depending on the driver, he might even stop for brief rest to enjoy the view or spend some time on a leisure activity. These types of stops enhances the driving pleasure and are determined subjectively based on the individual driver. The driver can input desired leisure spots into the Car Navigation System. This input causes the Car Navigation System to plan desired stops along the final destination point. However after driving, the driver sometimes is not satisfied by the route chosen by the Car Navigation System. The dissatisfaction is caused by the system's inability to anticipate the driver's immediate need. For example, based on the driver's pretend stop points, the system may direct the driver to a tennis court, but by the time of arrival, it could be raining or the road driven may be congested, slowly the drive. In these cases, the drive to the courts would have been time wasted. The driver's desired stop point is subjective input while the change in weather change or traffic patterns are objective elements. The destination decided is influenced by both of these factors. Even if these are many objective elements, they are transitional so events that may influence can be predicted. Therefore, the function and the concept of the current Car Navigation System were examined. Although the system's function can guide the driver to the desired destination points, but it cannot forecast changes in driving conditions. In the future, there is a prospect for a new function of Car Navigation System based on model designed using an AHP.

The car navigation service has opened through the global positioning system (GPS). People's lives have changed through car navigation services, and this service is indispensable nowadays. GPS provides reliable location accuracy in open space, but its location accuracy is degraded where multipath and other error factors are present. In particular, since GPS signals must be received in order to calculate the position, the navigation system cannot calculate the position in an indoor space such as underground parking lot, subway or tunnel where GPS signals are cut off. When car navigation is performed in an indoor parking lot where GPS signals are not transmitted, the user cannot calculate the current location, so the user has to go out by relying on the traffic sign because he does not know the current location. Also, when the user leave the parking lot, the car navigation system has problems. In an urban canyon, it is difficult to calculate accurate current position due to multipath. Because the route is calculated through the current location and route, the car navigation system provides the wrong route to the user. In addition, when entering a tunnel while using car navigation, the car navigation system calculates the position using the initial tunnel entry velocity estimated by the GPS. As the speed of the user increases or decreases, the position error also increases. Then the position suddenly changes when passing through the tunnel. And it is difficult to provide a proper guidance at the branch road or exit in the tunnel because of the positioning error. People are demanding a complete car navigation service, but the current navigation service provides the wrong route and position information to the user where the GPS signal quality is poor. In order to solve the above problem, in this paper, we have studied to improve the user's position accuracy by using LTE signal and mobile phone internal sensor. In general, observed time difference of arrival (OTDOA) technique is applied for the LTE based positioning. Because this method gives tens of meters of error, it is difficult to be used for the car navigation service. In our research, we estimate the position using the LTE signal strength. The difference from conventional fingerprinting method is that instead of using only current signal strength, the signal strength pattern accumulated while moving is utilized to estimate the user position. The signal strength increases as the user approach the access point (AP), and decreases as the user move away from the AP. Therefore, when a user moves and stores signal strength with respect to a specific AP, a pattern that could occur only in the corresponding region is saved. There are many similar candidates in the database when using only the current signal strength. However, when using a pattern, similar candidates exist only around the true position. And, when using several LTE signals, the positioning accuracy could be increased. Since the user receives multiple LTE signals, the pattern becomes surface. The surface is compared with the database, and the position with the highest correlation value is estimated as the current position. We call this method surface correlation (SC). In order to verify the performance of the proposed method, experiments were conducted in the Gangnam beltway tunnel in Seoul. The testbed consisted of three tunnel and total distance is almost 11.8 Km. In the tunnel, repeater and several antennas are installed to widen the area of the LTE signal. When an LTE signal is received by the vehicle, the largest signal strength is received every time when the vehicle passes the nearest antenna. As a result, for a specific LTE signal, when the vehicle has passed through the LTE signal region, a unique pattern with multiple peaks is generated. In order to estimate the position, we first estimate the position with the SC technique. Then, the estimated position is corrected by comparing the peak of the currently received pattern with the peak of the database. Through this method, the position was estimated with an error of less than 50meter in the tunnel. Conclusion In this research, the LTE signal strength based positioning technology is presented. Especially, this technology, SC, provides the positioning performance that could be used for the car navigation in the tunnel where GPS signal is blocked. We expected that user would be use the accurate car navigation service through this technology in the GPS challenging environment.

The non-line-of-sight (NLOS) propagation caused by intermittently blocking of the direct path between the base station and the mobile station is the main difficulty for mobile user location in cellular wireless communication system. So it is indispensable to investigate the techniques about the NLOS error identification and correction. In this paper, we suggest a robust and accurate approach using probability density function (PDF) estimator to identify the NLOS situation, and then an improved biased Kalman filter which depends on PDF estimator as the nonlinearity weighting coefficient in recursive operation is used to mitigate the NLOS error for time of arrival (TOA) measurements. As the PDF estimator is calculated from actual TOA samples, it is adaptive for the complex practical NLOS environments. The simulation results also indicate that with the less prior information about communication environments, the approach we present has a good location performance even in severe NLOS situations.

The present paper considers how to improve estimation accuracy in a time of arrival (TOA) localization system using UWB in a non-line-of-sight (NLOS) environment for medical network. This method consists of step-by-step compensation on the basis of two approaches considering a reference position that is estimated from data affected by NLOS delay. The first consideration consists of determining NLOS delays for each node, performing compensation to alleviate the effect on line of sight (LOS) nodes through a step-by-step compensation for the NLOS delay. The second consideration consists of compensating the effect of NLOS delay on the position determined on the basis of node distribution and geometrical relations of the estimated positions. Using these considerations, we show that the proposed method outperforms the conventional NEWTON method in terms of estimation accuracy. Under certain conditions, it also exhibits better characteristics than the maximum likelihood estimation method, where environmental parameters such as NLOS delays are known.

We perform a novel indoor channel measurement campaign to characterize the channel impairments for time-of-arrival (ToA) localization. We use precisely calibrated and time-synchronized RF equipment for transmitting and receiving 10 MHz and 100 MHz bandwidth waveforms over the 5.5 GHz DFS band in line-of-sight (LOS) and non-LOS environments. We collect ToA bias statistics with respect to a direct LOS path, where the bias is caused by multipath and NLOS reflections. Indoor wall reflections create significant ToA bias error of up to a couple hundred nanoseconds, corresponding to a positive ranging error of several tens of meters. We show how the WINNER II channel model, commonly used for communications simulations, is not suitable for ToA localization due to the improperly modeled signal timing and unrealistically low bias. The bias statistics based on our measurements are used for simulations of a ToA localization system using conventional and probabilistic multilateration algorithms. We present numerical results which provide insights for system design and performance-cost tradeoffs with regard to infrastructure density and signal bandwidth.

A novel hybrid wireless positioning method in non-line of sight (NLOS) situation is proposed in this paper. Firstly, a Kalman filter (KF) is used to track the NLOS bias in the time of arrival (TOA) measurements so as to mitigate the NLOS error. Secondly, another KF combines the most three common positioning methods, the time difference of arrival (TDOA) positioning, the TOA positioning and the angle of arrival (AOA) positioning, to achieve high location accuracy. Bused on linear forms of the measurement equations, the location estimator that use the linear KF (LKF) rather than the extended KF (EKF) is developed, thus the computation can be simplified. Simulation shows that the proposed hybrid method gives good performance.

When there is non-line-of-sight (NLOS) path between the mobile station (MS) and base stations (BSs), it is possible to integrate many kinds of measurements to achieve more accurate measurements of the MS location. This paper proposed hybrid methods that utilize time of arrival (TOA) at five BSs and angle of arrival (AOA) information at the serving BS to determine the MS location in NLOS environments. The methods mitigate the NLOS effect simply by the weighted sum of the intersections between five TOA circles and the AOA line without requiring priori knowledge of NLOS error statistics. Simulation results show that the proposed methods always give superior performance than Taylor series algorithm (TSA) and the hybrid lines of position algorithm (HLOP).

This paper presents field test results of mobile positioning with signals of opportunity (SOOP) in urban and urban canyon environments and the lessons learned. The particular SOOP considered in this paper is the digital television (DTV) signals available in the United States, namely, ATSC-8VSB. The field tests include runs in downtown San Mateo and San Francisco Financial District, representing typical urban and urban canyon environments. DTV signals from six DTV stations together with IMU and GPS data are recorded aboard a ground vehicle. The field tests show that DTV signals are abundant in urban and urban canyon environments, contrary to a popular concern about signal availability due to blockage by high-risers. Positioning geometry is also not a problem even though the number of transmitters may be limited because the ranges to signal sources are relatively short and the resulting geometric dilution of precision (GDOP) is acceptable especially for the two-dimensional solutions. However, multipath is omnipresent, having two detrimental effects on ranging and positioning. Severe mobile fading is frequent that disrupts continuous tracking and non-line of sight (NLOS) signals introduce large errors to correlation peak based timing. To process multipath-dominant signals, a signal parameter estimation methodology is developed. In this approach, periodic signal patterns (field sync segments at 41 Hz) are searched for via correlation using a constant false alarm rate (CFAR) detector and their times of arrival (TOA) are extracted using the orthogonal matched pursuit (OMP) algorithm amidst multipath to form pseudorange measurements. Both standalone SOOP solutions and integrated SOOP/IMU solutions are generated for the test trajectories, which are then compared to GPS for performance evaluation. The study indicates that the processing algorithms presented in the paper can successfully detect the first arrival of SOOP in urban and urban canyon environments and extract its TOA precisely when the signals are available (out of fading or blockage). However, the timing information carried by such first arrivals, good enough to serve the primary purpose of communications, may not be so for ranging because of NLOS. As such, timing of first arrivals is not sufficient by itself for positioning and has to be used in conjunction with other data such as IMU, which is used in this paper. Yet, distinct and stable multipath signatures may be exploited, together with an environment map for instance, for persistent positioning in urban and urban canyon environments, which is a direction of our future research.

Based on the positive bias property of the time of arrival(TOA) measurement error caused by the non-line-of-sight(NLOS) propagation, a simple and effective three dimensional(3D) geometrical localization algorithm was proposed, the algorithm needs no prior knowledge of time delay distribution of TOA, and only linear regression was used to estimate the parameters of the relationship between the NLOS distance error and the true distance, thus, the approximate real distance between mobile terminal (MT) and base station (BS) was reduced, then, the 3D geometric localization of mobile terminal was carried out by the least square method. The experimental results shows the effectiveness of the algorithm, and the positional accuracy is far higher than the required accuracy by E-911 in NLOS environments.

In this thesis, we cope with the fundamental mathematical and physical problems of the space-time-Doppler UWB system design for fading channels: multi-input multi-output (MIMO) wireless communication with adaptive filtering. The estimation algorithm from equalizer providing an accurate parameters estimate is investigated. UWB radio is a fast emerging technology with uniquely attractively features inviting major advances in wireless communications, networking, radar, imaging, and positioning systems. By its rule-making proposal in 2002, the Federal Communications Commission (FCC) in the United States essentially unleashed huge new bandwidth (3.6-10.6 GHz) at the noise floor, where UWB radios overlaying coexistence RF systems cerate using low-power ultra-short information bearing pulses. Right now, there are three basic types of UWB: multiband OFDM UWB (MB-OFDM UWB), impulse-UWB (I-UWB), and direct sequence UWB (DS-UWB). We focus on I-UWB and MB-OFDM-UWB, and compare their performance. A simple means for spreading the spectrum of low duty cycle pulse trains is time hopping (TH), with pulse position modulation (PPM) for data modulation at the rate of many pulses per data bit. The main contribution of this thesis for I-UWB is the design of leading edge detectors, coherent correlation detector (CCD), and pilot performance enhancement compared with different Rayleigh and Ricean fading conditions. For MB-OFDM UWB, we have considered the performance analysis and design criteria of MIMO OFDM UWB systems for high data-rate wireless transmission. The signal processing of frame detection, time synchronization, frequency synchronization, channel estimation, synchronization tracking using pilot subcarriers and 2-D MIMO detection algorithms is discussed. The main contributions of this thesis are 1) Compared with receiver employing the Maximum Likelihood (ML) detector, the receiver employing a low-complexity linear Minimum Mean Square Error (MMSE) demodulator can perform a limited performance loss in spatially uncorrelated channels. 2) Due to the operations of spreading and dispreading combined with MMSE-based frequency domain equalization, the adverse effects of the low-SNR subcarriers on the average BER performance are potentially improved. This is a direct consequence of spreading, because even if the signal corresponding to a specific chip is obliterated by a deep frequency domain channel fade, after dispreading these effects are spread over the Walsh-Hadamard Transform (WHT) length. Hence there is a high chance of still recovering all the partially affected subcarrier symbols without errors. 3) As a test case, the OFDM-based 200 Mb/s MB-UWB network (IEEE 802.15.3a) are considered, but the simulation results are shown above section. 4) The simulation channel can be easily applied to NOS and NLOS situation with WHT transformation from Simulink model. The fading channels of UWB with low/high signal to noise ratio (SNR), multipath effects, and multiuser interference may introduce large errors in location. The location engine performs the following tasks: █ Data fusion techniques: Different data fusion techniques are implemented using Time Of Arrival (TOA), Angle Of Arrival (AOA), or a combination of both. █ Channel modeling: A multipath, multiuser channel environment is created that models path loss, shadowing, Rayleigh fading, and Doppler frequency effects. █ Parameter estimation: TOA and AOA estimation algorithms are implemented as part of the location finding engine. Different variations of algorithms are implemented for performance and comparison purposes. █ Configuring the physical layer for different wireless networks. Among the programmable parameters are spreading factor, packet size, training length, constellation type, modulation technique, carrier frequency, level of transmitted signal power, and the number of antennas at the transmitter for both sides. █ Configuring the mobile user conditions. The wireless channel models depend on the Doppler frequencies present in the environment. The Doppler frequency depends on the mobile speed and carrier frequencies as input and generates a Rayleigh fading channel with the U-shape Doppler spectrum. █ Configuring the environmental parameters and network geographical structure. One of the factors affecting the performance of the wireless location system is the environment type (e.g., bad urban, urban, suburban, or rural area). For example, in a bad urban area with many blocking objects and buildings, non-line of sight (NLOS) effects plays an important role in the estimation accuracy.

As one of the core technologies of the Internet of Things, wireless sensor network technology is widely used in indoor localization systems. Considering that sensors can be deployed to non-line-of-sight (NLOS) environments to collect information, wireless sensor network technology is used to locate positions in complex NLOS environments to meet the growing positioning needs of people. In this paper, we propose a novel time of arrival (TOA)-based localization scheme. We regard the line-of-sight (LOS) environment and non-line-of-sight environment in wireless positioning as a Markov process with two interactive models. In the NLOS model, we propose a modified probabilistic data association (MPDA) algorithm to reduce the NLOS errors in position estimation. After the NLOS recognition, if the number of correct positions is zero continuously, it will lead to inaccurate localization. In this paper, the NLOS tracer method is proposed to solve this problem to improve the robustness of the probabilistic data association algorithm. The simulation and experimental results show that the proposed algorithm can mitigate the influence of NLOS errors and achieve a higher localization accuracy when compared with the existing methods.

The present paper considers how to improve estimation accuracy in a time of arrival (TOA) localization system using UWB in a non-line-of-sight (NLOS) environment for a sensor network. This algorithm consists of step-by-step compensation on the basis of two approaches considering a reference position which is estimated from data affected by NLOS delay. The first consideration consists of determining NLOS delays for each node, performing compensation to alleviate the effect on line of sight (LOS) nodes through a step-by-step compensation for the NLOS delay. The second consideration consists of compensating the effect of NLOS delay on the position determined on the basis of node distribution and geometrical relations of the estimated positions. Using these considerations, we show that the proposed algorithm outperforms the conventional algorithm in terms of estimation accuracy.

Hybrid particle filtering algorithm for GPS multipath mitigation

A new multipath mitigation algorithm for composite binary offset carrier (CBOC) signals defined for the Galileo navigation system is presented. The pilot and data channels are jointly tracked by multipath mitigation windows (MMW). The proposed algorithm is demonstrated as an implementation of the optimum s-curve shaping for CBOC multipath mitigation applications.

The purpose of a localization system is to estimate the coordinates of the geographic location of a mobile device. The accuracy of wireless localization is influenced by nonline-of-sight (NLOS) errors in wireless sensor networks. In this paper, we present an improved time of arrival (TOA)-based localization method for wireless sensor networks. TOA-based localization estimates the geographic location of a mobile device using the distances between a mobile station (MS) and three or more base stations (BSs). However, each of the NLOS errors along a distance measured from an MS (device) to a BS (device) is different because of dissimilar obstacles in the direct signal path between the two devices. To accurately estimate the geographic location of a mobile device in TOA-based localization, we propose an optimized localization method with a BS selection scheme that selects three measured distances that contain a relatively small number of NLOS errors, in this paper. Performance evaluations are presented, and the experimental results are validated through comparisons of various localization methods with the proposed method.