Post on 03-Jan-2022
Network DesignMotivation and Contributions
Signal Model Antenna Selection and DOA Estimation
User Interface Results
High DOA resolution requires large number of antennas. Multiple antennas usually entails dedicated hardware
equipment for each radar receive antennas which results in
high cost.
To achieve a balance between hardware cost and
resolution, it is often desirable to choose an optimum
subarray from a full array based on the target scenario.
To optimally choose the subarrays based on the target.
DOAs, we design a convolutional network which accepts
the array covariance matrix as an input and selects the best
sparse subarray that minimizes the mean square error.
This approach aids in cognition where multiple antennas
are simultaneously operate to track different targets in
different directions based on the current target scenario.
We design a deep neural network which consists of 9 layers. The CNN accepts the two-dimensional inputs in three
real-valued channels.
There are rectified linear units (ReLU) after each convolutional and fully connected layers.
At the output layer, there are Q units wherein the network classifies the given input data using a softmax function and
reports the probability distribution of the classes to provide the best subarray.
Deep Sparse Antenna Array Selection And DOA Estimation DemoSatish Mulleti1, Regev Cohen2, Ahmet M. Elbir3, Rong Fu4, Moshe Namer2, Maxim Meltsin2, Harel Moalem2,
Eli Laks2, Eli Shoshan2, Tinayao Huang4, Yimin Liu4, Yonina C. Eldar1
1 Faculty of Mathematics and Computer Science, Weizmann institute of Science, Rehovot, Israel2 Andrew and Erna Viterbi Faculty of Electrical Engineering, Technion-Israel Institute of Technology, Haifa, Israel.
3 Department of Electrical and Electronics Engineering, Duzce University, Duzce, Turkey.4 Department of Electronic Engineering, Tsinghua University, Beijing, China.
* e-mail: mulleti.satish@gmail.com
Demo System
The 16 Tx and Rx signals from the up-converters, are amplified and
connected 16 sets of 4 patch antennas each. Only one antenna of each
set can be selected at a time, via 4 to 1 high speed selector. For high
speed operation there is a local FPGA, enabling to run pre-defined
cyclic scenarios and select specific antennas.
Three antenna selection methods are applied
Best subarray gives lowest CRB.
Random subarray is selected by random array
selection (RAS).
CNN subarray is selected by trained CNN network,
which input dataset are three real-valued channels
where, ๐ is the sample data covariance
Two DOA estimation algorithms:
MUSIC
๐ ฮ : the steering vector,
๐ฎ:the noise subspace eigenvector matrix of the sample data covariance
FISTA
๐: K-sparse vector with the same support as of target signal
๐ฉ=๐จโ๐จ, ๐คโ๐๐๐ ๐จ is the array manifold matrix
๐: the vectorization of the sample data covariance ๐
Once the optimum sparse subarray is obtained, the
signals from the selected antennas are used to estimate
the DOAs. We provide numerical simulations to validate
the performance of the proposed cognitive array selection
strategy and different DOA estimation algorithm.
The RMSE vs SNR plots for MUSIC and FISTA are shown
in Fig. A and B, respectively.
The proposed CNN approach effectively selects the best
subarray for a large range of SNRs and it provides
effective performance as compared to RAS.
For a given antenna selection method, comparing the
DOA estimation performance of MUSIC and FISTA, it is
observed that the MUSIC algorithm performs better than
FISTA. The RMSE of FISTA does not reduce for high SNR
while MUSIC provides much better precision.
๐ ฮ =1
๐๐ป ฮ ๐ฎ๐ฎ๐ป๐ ฮ
เท๐ = ๐๐๐๐๐๐๐โฅ๐
๐ฉ๐ โ ๐ 22+๐ ๐ 1
๐1 ๐,๐ = โ ๐ ๐,๐
๐2 ๐,๐ = ๐ ๐ ๐ ๐,๐
๐3 ๐,๐ = ๐ผ๐ ๐ ๐,๐
๐ฆ(๐ก) = ฯ๐=1๐ ๐ฅ๐ ๐ก ๐ ฮ๐ + ๐ค(๐ก),
= ฯ๐=1๐ ๐จ๐ฅ ๐ก + ๐ค(๐ก),
where, ๐ ฮ is the steering vector and ๐จ is the
array manifold matrix,
๐จ = [๐ ฮ1 ๐ ฮ2 โฆ๐ ฮ๐ ]๐ฏ
For ๐ targets, the measurement vector at ๐กth
snapshot is given as:
Our goal is to estimate DOAs from the
autocorrelation matrix ๐ = ๐ธ ๐ฆ ๐ก ๐ป๐ฆ ๐ก of the
measurements.
Antenna
array GUI
Rx FPGA
Tx FPGA
Rx antenna array
FPGA and UP/DOWN converters
(A)
(B)