TDOA positioning accuracy performance evaluation for arc sensor configuration

Accurate remote determination of the object coordinates in 3D space is one of the main questions in many applications. In one of the most popular methods, such determination of the location of an object uses the measurement by receiving an electromagnetic signal transmitted by several spatially distributed base stations (BS). The main problem is that it is necessary to reduce errors and computation time. To overcome these difficulties, an analytical method for determining the position of an object based on the analysis of time difference of arrival (TDoA) of signals from the transmitter of the object to the receivers of the BS is proposed. One of the main advantages of this method is that it is possible to eliminate the ambiguity in determining the coordinates of the object in space and to increase the accuracy of determining the coordinates when the TDoA measurement between base stations fluctuates. Applications for autonomous automotive vehicles and spacebased positioning systems are analyzed. The results obtained show that the proposed algorithm has an accuracy of determining coordinates several times higher than the method of linearization of hyperbolic equations and is less sensitive to TDoA fluctuations at base stations.

Section: Resultsmentioning

confidence: 99%

Method for Remote Determination of Object Coordinates in Space Based on Exact Analytical Solution of Hyperbolic Equations

Kuptsov

Badenko

Ivanov

et al. 2020

“…Instead, the propagation graph in our case can directly generate the channel impulse responses (CIRs) in a deterministic way, which more accurately describes how the wave propagates in the area. In [27], a path loss model featuring an urban-macro-cell-LoS environment was exploited in the radio channel simulation for the algorithm evaluation. However, the fading model was unable to provide the propagation delay of the signals utilized by the TDoA technique.…”

Section: Using Combinations Of Simulations and Experimental Validationmentioning

confidence: 99%

“…A large number of contributions have been made on the performance evaluation of TDoA localization systems [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]. They can be categorized into the simulation, the experiment and the combinations of these two.…”

Section: Introductionmentioning

confidence: 99%

“…Despite the ability to consider real wave propagation and practical system errors, the experiments show an operation inconvenience as well as high cost, and the measurement results obtained in one area can not be applied to another. Other approaches integrating the simulation and experiment [24][25][26][27][28][29] wisely apply statistical radio channel models obtained from real channel measurements to the simulation-based performance evaluation, or provide the statistical characteristics of algorithms or systems through Monte Carlo simulations before conducting experiments. However, the hybrid approaches display the inapplicability of providing a practical localization system with deterministic information of the wave propagation for a specific area.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Experiment-Free Site-Specific TDoA Localization Performance-Evaluation Approach

Rodríguez-Piñeiro

Liu

et al. 2020

Time difference of arrival (TDoA) technology is widely utilized for source localization, which stimulates many studies on performance-evaluation approaches for TDoA localization systems. Some approaches using simulations are designed merely for a simple Line-of-Sight (LoS) scenario while some other ones using experiments show high cost and inefficiency. This paper proposes an integrated approach to evaluate a TDoA localization system in an area with a complicated environment. Radio propagation graph is applied through a simulation to obtain channel impulse responses (CIRs) between a source to be located and the TDoA sensors for the area. Realistic signals received by the sensors in baseband are emulated combining the source transmitted signal and the CIRs. A hardware unit takes charge of sending the radio emulated received signals to the system under test, which is consistent with real experimental measurements. Statistical analysis of the system is allowed based on localization errors obtained comparing the system’s estimates with the ground truth of the source location. Verified results for LoS and non-LoS scenarios with variable transmitted signal bandwidths and signal-to-noise ratios, as well as for three variations of the sensor locations in an automobile circuit, show the usability of the proposed experiment-free performance-evaluation approach.

“…In the actual scenario of positioning based on TDOA delay estimation, the signal model [ 21 , 22 , 23 ] can be expressed as:

where

is the target signal,

and

are the two acquisition signals, and

and

are noise. Assuming that the noise and the target signal are uncorrelated, D is the time delay and A is the attenuation factor.…”

Section: Theoretical Introduction Of Delay Estimation Optimization Al...mentioning

confidence: 99%

Optimization Algorithm for Delay Estimation Based on Singular Value Decomposition and Improved GCC-PHAT Weighting

Wang

et al. 2022

The accuracy of time delay estimation seriously affects the accuracy of sound source localization. In order to improve the accuracy of time delay estimation under the condition of low SNR, a delay estimation optimization algorithm based on singular value decomposition and improved GCC-PHAT weighting (GCC-PHAT-ργ weighting) is proposed. Firstly, the acoustic signal collected by the acoustic sensor array is subjected to singular value decomposition and noise reduction processing to improve the signal-to-noise ratio of the signal; then, the cross-correlation operation is performed, and the cross-correlation function is processed by the GCC-PHAT-ργ weighting method to obtain the cross-power spectrum; finally, the inverse transformation is performed to obtain the generalized correlation time domain function, and the peak detection is performed to obtain the delay difference. The experiment was carried out in a large outdoor pool, and the experimental data were processed to compare the time delay estimation performance of three methods: GCC-PHAT weighting, SVD-GCC-PHAT weighting (meaning: GCC-PHAT weighting based on singular value decomposition) and SVD-GCC-PHAT-ργ weighting (meaning: GCC-PHAT-ργ weighting based on singular value decomposition). The results show that the delay estimation optimization algorithm based on SVD-GCC-PHAT-ργ improves the delay estimation accuracy by at least 37.95% compared with the other two methods. The new optimization algorithm has good delay estimation performance.