70,654 research outputs found
LADAR vision technology for automated rendezvous and capture
LADAR Vision Technology at Autonomous Technologies Corporation consists of two sensor/processing technology elements: high performance long range multifunction coherent Doppler laser radar (LADAR) technology; and short range integrated CCD camera with direct detection laser ranging sensors. Algorithms and specific signal processing implementations have been simulated for both sensor/processing approaches to position and attitude tracking applicable to AR&C. Experimental data supporting certain sensor measurement accuracies have been generated
Radar Signal Processing for Interference Mitigation
It is necessary for radars to suppress interferences to near the noise level to achieve the best performance in target detection and measurements. In this dissertation work, innovative signal processing approaches are proposed to effectively mitigate two of the most common types of interferences: jammers and clutter. Two types of radar systems are considered for developing new signal processing algorithms: phased-array radar and multiple-input multiple-output (MIMO) radar. For phased-array radar, an innovative target-clutter feature-based recognition approach termed as Beam-Doppler Image Feature Recognition (BDIFR) is proposed to detect moving targets in inhomogeneous clutter. Moreover, a new ground moving target detection algorithm is proposed for airborne radar. The essence of this algorithm is to compensate for the ground clutter Doppler shift caused by the moving platform and then to cancel the Doppler-compensated clutter using MTI filters that are commonly used in ground-based radar systems. Without the need of clutter estimation, the new algorithms outperform the conventional Space-Time Adaptive Processing (STAP) algorithm in ground moving target detection in inhomogeneous clutter.
For MIMO radar, a time-efficient reduced-dimensional clutter suppression algorithm termed as Reduced-dimension Space-time Adaptive Processing (RSTAP) is proposed to minimize the number of the training samples required for clutter estimation. To deal with highly heterogeneous clutter more effectively, we also proposed a robust deterministic STAP algorithm operating on snapshot-to-snapshot basis. For cancelling jammers in the radar mainlobe direction, an innovative jamming elimination approach is proposed based on coherent MIMO radar adaptive beamforming. When combined with mutual information (MI) based cognitive radar transmit waveform design, this new approach can be used to enable spectrum sharing effectively between radar and wireless communication systems.
The proposed interference mitigation approaches are validated by carrying out simulations for typical radar operation scenarios. The advantages of the proposed interference mitigation methods over the existing signal processing techniques are demonstrated both analytically and empirically
Passive Synthetic Aperture Radar Imaging Using Commercial OFDM Communication Networks
Modern communication systems provide myriad opportunities for passive radar applications. OFDM is a popular waveform used widely in wireless communication networks today. Understanding the structure of these networks becomes critical in future passive radar systems design and concept development. This research develops collection and signal processing models to produce passive SAR ground images using OFDM communication networks. The OFDM-based WiMAX network is selected as a relevant example and is evaluated as a viable source for radar ground imaging. The monostatic and bistatic phase history models for OFDM are derived and validated with experimental single dimensional data. An airborne passive collection model is defined and signal processing approaches are proposed providing practical solutions to passive SAR imaging scenarios. Finally, experimental SAR images using general OFDM and WiMAX waveforms are shown to validate the overarching signal processing concept
Compressive Sensing for MIMO Radar
Multiple-input multiple-output (MIMO) radar systems have been shown to
achieve superior resolution as compared to traditional radar systems with the
same number of transmit and receive antennas. This paper considers a
distributed MIMO radar scenario, in which each transmit element is a node in a
wireless network, and investigates the use of compressive sampling for
direction-of-arrival (DOA) estimation. According to the theory of compressive
sampling, a signal that is sparse in some domain can be recovered based on far
fewer samples than required by the Nyquist sampling theorem. The DOA of targets
form a sparse vector in the angle space, and therefore, compressive sampling
can be applied for DOA estimation. The proposed approach achieves the superior
resolution of MIMO radar with far fewer samples than other approaches. This is
particularly useful in a distributed scenario, in which the results at each
receive node need to be transmitted to a fusion center for further processing
A novel approach of radar noise removal via sensor fusion and neural networks
Millimeter-Wave (mmWave) radar sensors are getting more popular for their increasing sensing
capabilities especially in automotive fields. However, no matter how ideal the radar signal
processing stack is, radar still suffers from internal and external noises which degrades its potential
sensing solutions. There are certainly many traditional approaches to reduce radar noises like
matched filter, but this paper will present a novel approach of noise removal via sensor fusion and
artificial neural networks.Ope
GroundâPenetrating Radar for Closeâin Mine Detection
In this chapter, two of the major challenges in the application of groundâpenetrating radar in humanitarian demining operations are addressed: (i) development and testing of affordable and practical ground penetrating radar (GPR)âbased systems, which can be used offâground and (ii) development of robust signal processing techniques for landmines detection and identification. Different approaches developed at the Royal Military Academy in order to demonstrate the possibility of enhancing closeârange landmine detection and identification using groundâpenetrating radar under laboratory and outdoor conditions are summarized here. Data acquired using different affordable and practical GPRâbased systems are used to validate a number of promising developments in signal processing techniques for target detection and identification. The proposed approaches have been validated with success in laboratory and outdoor conditions and for different scenarios, including antipersonnel, lowâmetal content landmines, improvised explosive devices and real mineâaffected soils
Bistatic OFDM-based Joint Radar-Communication: Synchronization, Data Communication and Sensing
This article introduces a bistatic joint radar-communication (RadCom) system
based on orthogonal frequency-division multiplexing (OFDM). In this context,
the adopted OFDM frame structure is described and system model encompassing
time, frequency, and sampling synchronization mismatches between the
transmitter and receiver of the bistatic system is outlined. Next, the signal
processing approaches for synchronization and communication are discussed, and
radar sensing processing approaches using either only pilots or a reconstructed
OFDM frame based on the estimated receive communication data are presented.
Finally, proof-of-concept measurement results are presented to validate the
investigated system and a trade-off between frame size and the performance of
the aforementioned processing steps is observed.Comment: Accepted for presentation at the focused session "Joint Communication
and Radar Sensing - a step towards 6G'' of the EuMW 202
Impacts of Radar Echoes on Internal Calibration Signals in the TerraSAR-X Instrument
For calibrating and monitoring the required radiometric stability, the radar instrument of TerraSAR-X features an internal calibration facility coupling into an additional port of the TRMs. Calibration pulses are routed through the front-end to characterise critical elements and parameters of the transmit (TX) and receive (RX) path. Changes in the signal path appear due to thermal effects, degradation, or extreme conditions in space. Especially the front-end TRMs controlling the phased array antenna are of crucial significance for the instrument reliability.
There are many indications that the interference of the RX-Calibration signals is caused by an echo from a transmitted TerraSAR-X chirp pulse of the same data take. As consequently implemented in the TerraSAR-X system, different approaches solve these effects of signal interference. In orbit, the commanding sequence can be optimised for avoiding interference. At processing level, averaging techniques minimise the noise effects inside the calibration signals. This paper presents the effects of the radar echoes on the whole internal calibration process and how they can be detected and minimised
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