Damped Gauss-Newton based online ranging for point extraction from low SNR and high overlapping waveforms

Li, Xiaolu; Bi, Tengfei; Wang, Zining; Xu, Lijun; He, Yuehui

doi:10.1016/j.measurement.2022.111479

Cited by 13 publications

(7 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using Equation ( 16), it is possible to estimate the precision of distance measurements using a full-waveform LiDAR, with the echo pulse signal expressed as a second-degree polynomial, based only on the parameters the echo pulse and the receiver. In order to verify whether Equation ( 16) can be applied to real-life measurements, we attempted to predict the uncertainty of measurements presented in [13] and [14] using Formula (16). In the work presented in the article [13], in order to evaluate the precision of the SDPA algorithm, we have performed five trials with different sets of parameters of generated pulses and different SNR ratios.…”

Section: Discussionmentioning

confidence: 99%

“…The SDPA algorithm is based on the least square approximation of the shape of a digitally recorded echo of a light pulse by a seconddegree polynomial. The reason why the second-degree polynomial was chosen, instead of more commonly used Gaussian function [14], is that it sufficiently models the shape of a laser pulse and is less demanding in terms of computational effort and, therefore, we assumed that it would result in a faster algorithm. The approximation is performed on the selected subset of n recorded samples that is expected to contain the returning echo of a probing pulse.…”

Section: Assumptionsmentioning

confidence: 99%

“…This can be particularly challenging in LiDAR forestry scanning applications and airborne and satellite altimetry where a single laser pulse can encounter multiple targets and yield overlapping echo waveforms [6,7]. In such cases, methods of waveform segmentation and data point extraction such as deconvolution [20] are needed, and advanced algorithms, such as Damped Gauss-Newton Multi-echo Decomposition, have been proposed [14]. We have assumed that, for single target measurements, simple threshold or constant-fraction discriminators are sufficient to extract datapoints of the echo signal.…”

Section: Assumptionsmentioning

confidence: 99%

See 2 more Smart Citations

Analysis of the Impact of Changes in Echo Signal Parameters on the Uncertainty of Distance Measurements in p-ToF Laser Rangefinders

Muzal

Zygmunt

2022

Sensors

View full text Add to dashboard Cite

The article presents results of research on the influence of changes in parameters of the digitally recorded echo signals on the uncertainty of pulsed Time-of-Flight (p-ToF) laser distance measurements. The main objective of the study was to evaluate the distance calculation method developed by the authors. This method is based on the acquisition of the full-waveform of the echo pulse signal and approximation of its shape by the second-degree polynomial (we called it SDPA for short). To determine the pulse transit time and measure the distance, the position of the vertex of this parabola is sought. This position represents the maximum intensity of the incoming echo signal and is related to the round-trip propagation time of the laser pulse. In the presented work, measurement uncertainty was evaluated using simulation tests for various parameters of the echo pulse. All obtained results were used to formulate the general relationship between the measurement uncertainty of the SDPA algorithm and the parameters of the received echo signals. This formula extends the base knowledge in the domain of laser p-ToF distance measurements. It can be used to estimate the measurement uncertainty of a FW LiDAR at an early design stage. This greatly improves capabilities of analysis of expected performance of the device. It can also be implemented directly into the rangefinder’s measurement algorithm to estimate the measurement uncertainty based on the emission of a single pulse rather than a series of pulses.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Assumptionsmentioning

confidence: 99%

Section: Assumptionsmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of the Impact of Changes in Echo Signal Parameters on the Uncertainty of Distance Measurements in p-ToF Laser Rangefinders

Muzal

Zygmunt

2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Digital methods that determine the STOP signal can operate on a digitally recorded echo signal [13][14][15][16]. They also make it possible to distinguish overlapping echo signals [15,17], which is not possible in analog methods. Digital methods allow reconstructions of signals that bring detection channel amplifiers into saturation [18].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Walk Error Compensation Method Using Echo Signal Magnitude Measurement in ToF Laser Scanners

Sędek,

Zygmunt,

Jakubaszek

et al. 2024

Sensors

View full text Add to dashboard Cite

The rapid advancement of mobile laser scanner technology used for terrain mapping, among other things, imposes increasing requirements for scanning frequency and distance measurement accuracy. To meet these requirements, rangefinder modules are expected to operate with high echo signal dynamics and to allow accurate distance measurement even based on single-laser-pulse echo detection. Such performance can be potentially achieved using pulsed time-of-flight (ToF) laser rangefinders (LRF). In conventional ToF modules, however, the STOP signal (for time counter interruption) is generated using a straightforward fixed-threshold comparator method. Unfortunately, it corresponds to the so-called walk error, i.e., the dependence of the measured time of flight on the magnitude of the echo signal. In most ranging applications, however, the LRF detection channel can be exposed to an extremely large span of received echo power levels, which depend on the distance measured, type of target surface, atmospheric transmission, etc. Thus, the walk error is an inseparable element of the conventional ToF technique and creates a fundamental limit for its precision. This article presents a novel method of walk error compensation in real time. By using our authorial electronic circuit for measuring the magnitude of the echo signal, it is possible to effectively compensate for the walk error even when the echo signal brings the detection channel amplifiers into saturation. In addition, the paper presents a laboratory method for calibrating the walk error compensation curve.

show abstract

“…In 2016, Xu et al [23] modified the Levenberg-Marquardt algorithm and improved the convergence speed. In 2022, Li et al [24] modified the Gauss-Newton algorithm and proposed a damped-Gauss-Newton-based online ranging method. The sparse Jacobi matrix and the division-free Gauss-Jorden methods modify the iteration step.…”

Section: Introductionmentioning

confidence: 99%

Gaussian convolution decomposition for non-Gaussian shaped pulsed LiDAR waveform

Fang¹,

Wang²,

Zheng³

2022

Meas. Sci. Technol.

View full text Add to dashboard Cite

The full waveform decomposition technique is significant for LiDAR ranging. It is challenging to extract the parameters from non-Gaussian shaped waveforms accurately. Many parametric models (e.g. the Gaussian distribution, the lognormal distribution, the generalized normal distribution, the Burr distribution, and the skew-normal distribution) were proposed to fit sharply-peaked, heavy-tailed, and negative-tailed waveforms. However, these models can constrain the shape of the waveform components. In this article, the Gaussian convolution model is established. Firstly, a set of Gaussian functions is calculated to characterize the system waveform so that asymmetric and non-Gaussian system waveforms can be included. The convolution result of the system waveform and the target response is used as the model for fitting the overlapped echo. Then a combination method of the Richardson–Lucy deconvolution, layered iterative, and Gaussian convolution is introduced to estimate the initial parameters. The Levenberg–Marquardt algorithm is used for the optimization fitting. Through experiments on synthetic data and practical recorded coding LiDAR data, we compare the proposed method with two decomposition approaches (Gaussian decomposition and skew-normal decomposition). The experiment results revealed that the proposed method could precisely decompose the overlapped non-Gaussian heavy-tailed waveforms and provide the best ranging accuracy, component fitting accuracy, and anti-noise performance. However, the traditional Gaussian and skew-normal decomposition methods can not fit the components well, resulting in inaccurate range estimates.

show abstract

Damped Gauss-Newton based online ranging for point extraction from low SNR and high overlapping waveforms

Cited by 13 publications

References 20 publications

Analysis of the Impact of Changes in Echo Signal Parameters on the Uncertainty of Distance Measurements in p-ToF Laser Rangefinders

Analysis of the Impact of Changes in Echo Signal Parameters on the Uncertainty of Distance Measurements in p-ToF Laser Rangefinders

Real-Time Walk Error Compensation Method Using Echo Signal Magnitude Measurement in ToF Laser Scanners

Gaussian convolution decomposition for non-Gaussian shaped pulsed LiDAR waveform

Contact Info

Product

Resources

About