RADAR-target-generation-and-detection

In this project, an FMCW RADAR is configured. The transmitted and received signals are processed using FFT and CFAR to determine the range and velocity of the target

Project overview

The above picture outlines the project pipeline.

FMCW RADAR system configuration

The initial range and velocity of the target are set to be:

• R = 120 meters
• V = -20 meters/sec

The bandwidth, chirp time and slope of the chirp signal are calculated according to the following formulas

where Rmax is the maximum range of the RADAR and c is the speed of light.

Modeling signal propagation for moving target scenario

The transmission and received signals are characterised by the following equations.

The beat signal is computed as a product of transmission and received signal. The result is characterised by the following equation:

FFT: Range estimation

A 1D FFT is implemented on the best signal to estimate the range of the target.

FFT implementation:

• The beat signal is reshaped into a 2D vector of size (Nr, Nd). where Nr is the sampling frequency per chirp and Nd is the total number of chirps in a chirp sequence.
• FFT is run on the beat signal along the range bins dimension (Nr)
• The output of the FFT is normalized
• Since the second half of the output is a mirror image of the first half, only the first half of the image is retained.
• The output is plotted
• The peak rightly estimates the range of the target as follows:

Range Doppler Map (RDM)

The Range Doppler map is generated by running a 2D FFT on the reshaped beat signal. The implementation is as follows:

• A 2D FFT is run over the beat signal of dimension (Nr, Nd)
• Only the first half of the output, in range dimension, is retained.
• The absolute value of the RDM is computed and the logarithmic values of it are computed.
• The resulting RDM is visualized as follows:

From the above image, it can be observed that the map has a significant amount of noise. A constant threshold factor to detect peaks can potentially lead to a high rate of false alarms. To significantly overcome this problem, an adaptive Constant False Alarm Rate filter is applied to RDM as shown in the next section

Continous False Alarm Rate (CFAR)

A 2D CFAR filter, that suppresses the noise and computes a dynamic threshold to maintain a constant false alarm rate is implemented as follows:

• A filter of following configuratin is considered:

• The number of training cells and guard cells are iteratively determined.
• The filter is slid over the complete RDM.
• The mean of all the training cells around each cell of interest is computed. This mean is considered as the initial threshold.
• An offset (Iteratively fine-tuned) is added to the threshold in the previous step, resulting in the final threshold.
• Thus a vector of thresholds for each cell of interest is computed.
• The RDM is filtered according to the latest, dynamic, threshold values. This results in the following map, which contains only the valid target signal:

Conclusion

Thus a pipeline is built to:

• Configure an FMCW RADAR
• Generate transmission and receiving a signal
• Estimate the range of the target
• Generate Range Doppler Map (RDM)
• Filter RDM through CFAR and determine the range and velocity of the object with a reduced number of false alarms