The positioning ability is essential in mobile networks. Providing the position of entities is in general a complex task that becomes especially hard in ad hoc networks. In these networks, the ...topology cannot be planned and network nodes usually present restrictions in terms of computation capabilities and autonomy that constrain the network lifetime. Passive algorithms have been proposed for minimizing the impact of location services in the network, i.e. reducing the location traffic and extending the network life. However, the accuracy of passive algorithms tends to be worse than the accuracy for active approaches. This paper studies the impact of several proposals focused on improving the accuracy of the passive TDOA algorithm. Results indicate that weighing the ordinary least squares algorithm with the appropriate factors could be enough for improving the accuracy of the algorithm in some scenarios.
As one of the most significant technology in wireless sensor networks (WSN), localization has drawn much attention. In this paper, received signal strength (RSS) values are used as the indicator of ...the distance between blind node and reference nodes. The position of blind node is calculated via multilateration algorithm (MA). In order to improve the accuracy, Kalman filter (KF) is utilized to estimate the actual position. Due to the flaw of the model, divergence phenomenon occurs when the moving direction of blind node changes. Therefore, Kalman filter algorithm performs badly in location and tracking. However, a novel method is proposed by using fading Kalman filter (FKF) and finally improves the accuracy of location.
MLAT (Multilateration) is becoming one of the leading surveillance techniques in the A-SMGCS (Advanced - Surface Movement Guidance & Control System), and play an important role in the future's ...tracking system. MLAT is based on the TDOA (Time Difference of Arrival). It is very important for MLAT to associate TOAs (Time of Arrival) from different ground stations. This paper designs an improved clustering method with elastic grouping logic to adaptively associate the TOAs. This method dynamically adjusts the group number based on the cost function. Experiment results show our improved method can not only increase correct association ratio, but also decrease the position error of MLAT.
Multilateration systems operate by determining distances between a signal transmitter and a number of receivers. In aerial surveillance, radio signals are emitted as Secondary Surveillance Radar ...(SSR) by the aircraft, representing the signal transmitter. A number of base stations (sensors) receive the signals at different times. Most common approaches use time difference of arrival (TDOA) measurements, calculated by subtracting receiving times of one receiver from another. As TDOAs require intersecting hyperboloids, which is considered a hard task, this paper follows a different approach, using raw receiving times. Thus, estimating the signal's emission time is required, captured as a common offset within an augmented version of the system state. This way, the multilateration problem is reduced to intersecting cones. Estimation of the aircraft's position based on a nonlinear measurement model and an underlying linear system model is achieved using a linear regression Kalman filter 1, 2. A decomposed computation of the filter step is introduced, allowing a more efficient calculation.
One of the most stringent requirements to a multilateration (MLAT) system is very high accuracy of target (emitter) localisation. In view of this, the potential accuracy of emitter localisation ...(PAEL) based on Cramer-Rao inequality is important to use. Its dependence on system geometry and time of arrival (TOA) measurement accuracy allows choosing reasonable system geometry and requirements for TOA measurements. PAEL for MLAT systems and wide area MLAT (WAM) systems with different geometry is considered including systems developed for the Marco Polo airport in Venice, Italy (as an example). The possibility of velocity determination using PAEL for landing and taking off aircrafts is also discussed.
MLAT (Multilateration) is a necessary technique in A-SMGCS (Advanced - Surface Movement Guidance & Control System) in the future. The precision of MLAT is related to GDOP (Geometric Dilution of ...Precision) and TOA (Time of Arrival) accuracy of its distributed stations. In view of the optimization among the multi-stations, an improved method based on fuzzy clustering is proposed. Different stations constitute different combinations and lead to different locating results. But the distribution based on combinations with good condition is collective and clustered. So the modified fuzzy clustering is used to analyze the distribution, abstract the stations with good condition and achieve the goal of multi-stations optimization. The experimental results based on the simulations and practical data illustrate the proposed method is not only feasible but also more accurate than the traditional optimization method based on GDOP only.
In multilateration tracking, an object, e.g., an airplane, emits a known reference signal, which is received by several base stations (sensors) located at known positions. The receiving times of the ...signal at the sensors correspond to the times of arrival (TOA) plus an unknown offset, because the emission time is unknown. Usually, for estimating the position of the object, the receiving times are converted to a larger number of time differences of arrival (TDOA) in order to eliminate the unknown offset. To avoid this conversion, the proposed approach directly uses the receiving times. This is achieved by 1. determining the optimal offset from the redundant measurements in closed form and 2. by considering a modified measurement equation. As a result, position estimation can be performed by optimal stochastic linearization.
The ldquosafe segment occupancy control systemrdquo is the basic component of an airport guidance system where all the moving aircrafts are kept safely separated by an empty taxiway segment: the ...center lights of such segment shall be ldquoredrdquo until the leading aircraft has cleared also the next segment. This requires a very accurate and reliable detection of each aircraft progression from one segment to the following one. The study is focused on the comparative performance analysis of a two-sensor detection system based on a light-weight SMR and a basic multilateration/ADS-B system.
In this paper, a particle swarm optimization (PSO) based multilateration algorithm is presented for a UWB communications based sensor network. The particle swarm uses simple operators and is a ...bottom-up approach for identifying the location in a 2D space. Hence the PSO uses less energy. For comparison we present two alternative approaches traditionally used for this problem. The first one is (a) traditional iterative least square algorithm, (b) a one step simple least square solution. With respect to least squares, PSO results in slightly less error than the traditional iterative least square approach making. However, it is computationally inexpensive making it a good choice for a wireless network of small devices. The PSO multilateration algorithm really improves localization error over the one step least square algorithm. The new algorithm can replace the traditional algorithm in different applications.
Position Estimation Sand, Stephan; Dammann, Armin; Mensing, Christian
Positioning in Wireless Communications Systems,
2014, 2014-03-25
Book Chapter
Odprti dostop
In this chapter, four static positioning estimation methods are discussed: Triangulation, trilateration, multilateration, and fingerprinting. Here, static means that a mobile terminal is stationary ...during position estimation. The first three methods use angle of arrival, time of arrival, and time difference of arrival measurements, respectively. For these three methods, navigation equations and analytical or numerical solutions are derived. For instance, the Gauss–Newton, steepest descent, Levenberg–Marquardt, and Newton–Raphson algorithms are presented in order to solve the nonlinear equations numerically. The chapter concludes with a brief review of performance bounds and measurements that are used within this book.
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