Matched filter module as an application of modern FPGA in radar systems

2018 ◽  
Author(s):  
Michał Knioła ◽  
Adam Kawalec
Keyword(s):  
Matched Filter ◽  
Radar Systems

scholarly journals Theoretical performance of reiterated LMMSE filtering and coded radar waveforms

2009 ◽  
Vol 1 (4) ◽  
pp. 353-359
Author(s):  
Mayazzurra Ruggiano ◽  
Emiel Stolp ◽  
Piet van Genderen
Keyword(s):  
Orthogonal Frequency Division Multiplexing ◽  
Matched Filter ◽  
Signal To Noise Ratio ◽  
Minimum Mean Square Error ◽  
Sidelobe Suppression ◽  
Radar Systems ◽  
Filter Output ◽  
Radar Waveforms ◽  
Filtering Technique ◽  
Theoretical Performance

Orthogonal frequency division multiplexing (OFDM) waveforms offer strong advantages for integrated communication and radar systems. However, they exhibit inherent high-range sidelobes after matched filtering when standard communication constellation symbols are used for the coding of the carriers. Consequently, they require filtering at the receiver that can serve for sidelobe suppression in order to avoid target masking. However, unmasking is not the only concern; it is crucial to evaluate the filtering scheme both in terms of sidelobe suppression capability and in terms of output signal-to-noise ratio. This last criterion is essential when aiming at also detecting weaker reflections. In this paper the theoretical performance of the reiterated filtering technique based on linear minimum mean square error (LMMSE) is derived and compared to the matched filter. The unmasking capabilities are relevant, but also output power figures. Complex-valued filter output peaks are also evaluated and compared to the matched filter output peaks. Moreover, the performance of reiterated LMMSE is evaluated for OFDM communication-encoded radar waveforms.

INTERFERENCE TO ACTIVE-PASSIVE RADAR SYSTEMS CREATED BY EMISSIONS FROM HF AND VHF BROADCASTING STATIONS

2020 ◽  
Vol 79 (10) ◽  
pp. 829-845
Author(s):  
V. I. Lutsenko ◽  
I. V. Lutsenko ◽  
A. V. Sobolyak ◽  
I. V. Popov ◽  
N. X. Ahn ◽  
...  
Keyword(s):  
Passive Radar ◽  
Radar Systems

Object Detection Using an Ideal Observer Model

2020 ◽  
Vol 2020 (16) ◽  
pp. 41-1-41-7
Author(s):  
Orit Skorka ◽  
Paul J. Kane
Keyword(s):  
Object Detection ◽  
Matched Filter ◽  
Image Sensors ◽  
Ideal Observer ◽  
Noise Power ◽  
Relative Evaluation ◽  
Detection And Identification ◽  
Cross Correlation Analysis ◽  
Observer Model ◽  
The Ideal

Many of the metrics developed for informational imaging are useful in automotive imaging, since many of the tasks – for example, object detection and identification – are similar. This work discusses sensor characterization parameters for the Ideal Observer SNR model, and elaborates on the noise power spectrum. It presents cross-correlation analysis results for matched-filter detection of a tribar pattern in sets of resolution target images that were captured with three image sensors over a range of illumination levels. Lastly, the work compares the crosscorrelation data to predictions made by the Ideal Observer Model and demonstrates good agreement between the two methods on relative evaluation of detection capabilities.

Automotive Radars

2017 ◽  
Author(s):  
Sujeet Patole ◽  
Murat Torlak ◽  
Dan Wang ◽  
Murtaza Ali
Keyword(s):  
Signal Processing ◽  
Pedestrian Detection ◽  
Radar Signal ◽  
Automotive Radar ◽  
Estimation Algorithms ◽  
Radar Systems ◽  
Starting Point ◽  
Processing Techniques ◽  
Automotive Radars ◽  
Signal Processing Techniques

Automotive radars, along with other sensors such as lidar, (which stands for “light detection and ranging”), ultrasound, and cameras, form the backbone of self-driving cars and advanced driver assistant systems (ADASs). These technological advancements are enabled by extremely complex systems with a long signal processing path from radars/sensors to the controller. Automotive radar systems are responsible for the detection of objects and obstacles, their position, and speed relative to the vehicle. The development of signal processing techniques along with progress in the millimeter- wave (mm-wave) semiconductor technology plays a key role in automotive radar systems. Various signal processing techniques have been developed to provide better resolution and estimation performance in all measurement dimensions: range, azimuth-elevation angles, and velocity of the targets surrounding the vehicles. This article summarizes various aspects of automotive radar signal processing techniques, including waveform design, possible radar architectures, estimation algorithms, implementation complexity-resolution trade-off, and adaptive processing for complex environments, as well as unique problems associated with automotive radars such as pedestrian detection. We believe that this review article will combine the several contributions scattered in the literature to serve as a primary starting point to new researchers and to give a bird’s-eye view to the existing research community.

A Power Allocation Approach for 3D Target Tracking in Multistatic Radar Systems

2014 ◽  
Vol 35 (4) ◽  
pp. 901-907
Author(s):  
Jun-kun Yan ◽  
Feng-zhou Dai ◽  
Tong Qin ◽  
Hong-wei Liu ◽  
Zheng Bao
Keyword(s):  
Target Tracking ◽  
Power Allocation ◽  
Radar Systems ◽  
Multistatic Radar ◽  
Allocation Approach
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