위치추정
이 섹션에는 레이다, 소나, 통신에서 객체 위치추정 및 추적을 수행하는 응용 사례가 나와 있습니다. 위치추정은 능동 시나리오 또는 수동 시나리오를 포함할 수 있습니다. 기법에는 도달 시간, 도달 시간 차, 도달 시간 합 측정이 포함됩니다.
추천 예제
Target Localization in Active and Passive Radars
Model radar networks, configure and propagate radar waveforms, and perform time-of-arrival and time-difference of arrival estimation and localization.
Source Localization and Tracking with Passive Receivers
Localize and track targets using a passive source localization (PSL) sensor network.
Device Localization in Wireless Systems
Build wireless sensor networks, configure and propagate wireless waveforms, and then perform TOA/TDOA estimation and localization.
Passive Bistatic Radar Localization Using OFDM Communication Signals
Demonstrate a passive bistatic radar system that utilizes a cellular tower as a separate signal source for three-dimensional target localization.
Multistatic Localization of a Ship Using GPS Illuminations
You locate a ship using received GPS signals reflecting off the ship.
Source Localization Using Generalized Cross Correlation
Determine the position of the source of a wideband signal using generalized cross-correlation (GCC) and triangulation. For simplicity, this example is confined to a two-dimensional scenario consisting of one source and two receiving sensor arrays. You can extend this approach to more than two sensors or sensor arrays and to three dimensions.
UWB Localization Using IEEE 802.15.4z
Estimate the location of a single device following the IEEE® 802.15.4z™ standard.
802.11az Three-Dimensional Tracking Using Time of Arrival Estimation
Use an IEEE 802.11az Wi-Fi network to track Wi-Fi devices in a three-dimensional space using time of arrival (TOA) estimation.
Locating an Acoustic Beacon with a Passive Sonar System
Simulate a passive sonar system. A stationary underwater acoustic beacon is detected and localized by a towed passive array in a shallow-water channel. The acoustic beacon transmits a 10 millisecond pulse at 37.5 kilohertz every second, and is modeled as an isotropic projector. The locator system tows a passive array beneath the surface, which is modeled as a uniform linear array. Once the acoustic beacon signal is detected, a direction of arrival estimator is used to locate the beacon.
Indoor Non-Line-Of-Sight Localization Using Deep Learning
To address the NLOS challenge, fingerprinting-based methods have gained popularity. Unlike traditional techniques that use low-dimensional range and angle features, fingerprinting can leverage high-dimensional signatures—such as channel state information (CSI) or range-angle heatmaps which encapsulate rich environmental information, including NLOS effects. Deep learning models excel at extracting meaningful patterns from these complex, high-dimensional inputs, enabling direct mapping from signal fingerprints to precise position estimates.
