Fusing active and passive measurements for drone localization

Abstract. The Microwave Emission Model of Layered Snowpacks (MEMLS) was originally developed for microwave emissions of snowpacks in the frequency range 5–100 GHz. It is based on six-flux theory to describe radiative transfer in snow including absorption, multiple volume scattering, radiation trapping due to internal reflection and a combination of coherent and incoherent superposition of reflections between horizontal layer interfaces. Here we introduce MEMLS3&a, an extension of MEMLS, which includes a backscatter model for active microwave remote sensing of snow. The reflectivity is decomposed into diffuse and specular components. Slight undulations of the snow surface are taken into account. The treatment of like- and cross-polarization is accomplished by an empirical splitting parameter q. MEMLS3&a (as well as MEMLS) is set up in a way that snow input parameters can be derived by objective measurement methods which avoid fitting procedures of the scattering efficiency of snow, required by several other models. For the validation of the model we have used a combination of active and passive measurements from the NoSREx (Nordic Snow Radar Experiment) campaign in Sodankylä, Finland. We find a reasonable agreement between the measurements and simulations, subject to uncertainties in hitherto unmeasured input parameters of the backscatter model. The model is written in Matlab and the code is publicly available for download through the following website: http://www.iapmw.unibe.ch/research/projects/snowtools/memls.html.

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Passive Sonar Multiple-Target Tracking with Nonlinear Doppler and Bearing Measurements Using Multiple Sensors

International Journal of Aerospace Engineering ◽

10.1155/2021/4163766 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Xiaohua Li ◽

Bo Lu ◽

Wasiq Ali ◽

Jun Su ◽

Haiyan Jin

Keyword(s):

Target Tracking ◽

Tracking System ◽

Data Association ◽

Measurement Data ◽

Target Range ◽

Multiple Target Tracking ◽

Multiple Target ◽

Multiple Sensors ◽

Passive Sensors ◽

Passive Measurements

The major advantage of the passive multiple-target tracking is that the sonars do not emit signals and thus they can remain covert, which will reduce the risk of being attacked. However, the nonlinearity of the passive Doppler and bearing measurements, the range unobservability problem, and the measurement to target data association uncertainty make the passive multiple-target tracking problem challenging. To deal with the target to measurement data association uncertainty problem from multiple sensors, this paper proposed a batch recursive extended Rauch-Tung-Striebel smoother- (RTSS-) based probabilistic multiple hypothesis tracker (PMHT) algorithm, which can effectively handle a large number of passive measurements including clutters. The recursive extended RTSS which consists of a forward filter and a backward smoothing is used to deal with the nonlinear Doppler and bearing measurements. The target range unobservability problem is avoided due to using multiple passive sensors. The simulation results show that the proposed algorithm works well in a passive multiple-target tracking system under dense clutter environment, and its computing cost is low.

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High-Precision Drill Bit Tracking

10.2118/204039-ms ◽

2021 ◽

Author(s):

Marianne Houbiers ◽

Sascha Bussat ◽

Florian Schopper ◽

Fredrik Hansteen

Keyword(s):

High Precision ◽

Lateral Position ◽

Optimal Placement ◽

Drill Bit ◽

Noise Sources ◽

Position Uncertainty ◽

Passive Seismic ◽

Continuous Stream ◽

Well Trajectory ◽

Passive Measurements

Abstract The lateral well position uncertainty of magnetic/gyro MWD measurements can often exceed the requirements regarding anti-collision, for optimal placement of infill wells between existing producers, or for hitting targets with limited geological extent. The positional uncertainty can be significantly reduced by implementing high-precision drill-bit localization using passive seismic data. Consequently, not only drilling risks can be reduced, but optimal reservoir drainage is ensured as well. By utilizing passive seismic recordings from the seafloor, we can "listen" to the noise generated by the BHA while drilling. Despite various noise sources in the vicinity (e.g. vessels and rigs), advanced data processing and the combination of hundreds of seafloor receivers spread above the ongoing drilling, enable us to detect the drilling signal and locate the drill bit. Whereas the magnetic and gyro MWD tools have errors that accumulate with measured depth, each bit position derived from seismic (usually every 90 seconds) is completely independent. For horizontal sections, the error does not increase with measured depth, and hence can provide improved lateral accuracy. No additional BHA tool is required and the measurements are neither dependent on the magnetic nor gravitational field. Moreover, the passive seismic measurements can be used to obtain an improved lateral well position estimate. This is done by optimizing the azimuth information of the well trajectory in the minimum curvature method. A lateral uncertainty measure can be derived from the residuals between the passive measurements and the updated well path. Since 2018, we have used the continuous stream of passive data from permanent seafloor sensors at the Grane field with its reservoir depth of around 1800 m TVDSS to follow all wells with this drill bit tracking scheme. Lateral deviations from the magnetic/gyro measurements of up to 20m have been observed. The lateral position uncertainty can be as low as a couple of meters under optimal conditions.

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