Method for estimating time of arrival of signal in strong multipath environment

Abstract

The invention discloses a method for estimating time of arrival of a signal in a strong multipath environment. According to the method, normalized matching and filtering are carried out on a received signal, so that the noise interference is reduced and the influence caused by signal transmission losses is reduced. Meanwhile, the maximum value and the minimum value of the receiving signal filter output are considered in terms of dynamic threshold setting, so that the channel response as well as the signal to noise ratio information is represented. Besides, with a maximum value detection backtracking method, a phenomenon that searching is started at the output starting point of the filter after threshold determination is avoided and the computing load is reduced. Because of a small backtracking range, burst noise peak values fall beyond the backtracking window in most cases, thereby avoiding the possible peak interference to the greatest extent and improving the TOA estimation efficiency and precision. The method has advantages of simple algorithm and accurate TOA estimation and the like. The experiment demonstrates that the method has the high TOA estimation precision on an indoor condition, so that the good foundation can be laid for realizing high-precision positioning.

Description

technical field [0001] The invention belongs to the technical field of signal detection and wireless positioning, and in particular relates to a signal arrival time estimation method in a strong multipath environment. Background technique [0002] Indoor positioning technology has received extensive attention in specific occasions. For the indoor environment, the satellite signal fades seriously, and GPS loses its function; at the same time, the positioning accuracy of the cellular base station is too low to meet the accuracy requirements of indoor positioning. Due to the needs of easy promotion and low-cost indoor positioning system, the use of acoustic signal as a positioning carrier is becoming one of the research hotspots. [0003] In the positioning system based on Time of Arrival (TOA), the distance between the target and the base station is estimated by the direct path (DirectPath, DP) propagation time, and the target position can be estimated using the trilateral po...

AI Technical Summary

Problems solved by technology

The likelihood estimation method tries to find a group of multipath components, and matches the observed signal samples through the delay and amplitude combination of the multipath components. The likelihood estimation method generally seeks the optimal multipath on the maximum likelihood rule through iteration. Combination, this method is computationally intensive, difficult to implement on mobile devices, and has poor performance under low SNR conditions

The threshold judgment method uses a correlator, that is, a matched filter (Matched Filter, MF). The existing threshold judgment method can be divided into three types: the first is the maximum energy selection method (MaximumEnergy Selection, MES). The maximum output of is used as the threshold, that is, the moment of maximum output is considered to be the arrival time of the signal. However, in the indoor multipath environment, the direct path is often not the strongest path. Using the MES algorithm will cause a huge error in TOA estimation; the second is the threshold The comparison method (Threshold Comparison, TC) compares the output of MF with a specific threshold, and selects the first value exceeding the threshold as the TOA estimate. In the TC method, the threshold is generally a function of the signal-to-noise ratio. This method is only used at high Under the condition of signal-to-noise ratio, higher accuracy can be obtained, and it cannot cope with burst noise; the third is based on the maximum energy backtracking algorithm (Maximum Energy Selection-Search Back, MES-SB), which locates the maximum output of MF Afterwards, search for a fixed length in the backward direction (delay reduction direction), and select the first moment below the threshold value, and the median value between this moment and the maximum output moment is used as the TOA estimate

The MES-SB method has better performance than the TC method under the condition of low signal-to-noise ratio, but its estimation result is always between the real TOA and the maximum output of MF, and it is usually impossible to obtain accurate TOA estimation

[0005] To sum up, the existing TOA estimation methods have certain difficulties in dealing with indoor strong multipath environments. In addition, previous studies have focused on white noise environments, and have not involved too much burst noise. In the existing TOA It is difficult to achieve accurate positioning based on the estimation method

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