1. A Multi-Channel Partial-Update Algorithm for
Sea Clutter Suppression in Passive Bistatic Radar
China Academy of Electronics and Information Technology
Speraker：Ma Yahui
2018.7

2. 1 INTRODUCTION 2 SIGNAL MODEL 3 2D-PU-NLMS ALGORITHM 4
REAL-DATA EXPERIMENT 5
CONCLUSION

3. One feasible solution:
1. INTRODUCTION
Problem:
Sea clutters with Doppler-varying spectrum exert a notable negative impact on the detection performance, when a PBR is employed to detect sea-surface targets with low-velocity targets.
One feasible solution:
Conventional 1D (Range dimension) adaptive filters (such as NLMS)
modulate the reference signal onto the Doppler dimension
Multi-channel NLMS (MC-NLMS or 2D-NLMS)
The added fully-updating counterparts in Doppler dimension induces high computational load
partial-update NLMS (PU-NLMS)
Our work:
PU-NLMS+ 2D-NLMS
Sea clutters
Passive bistatic radars (PBRs) exploit the transmitted signals from non-cooperative illuminators of opportunity to perform detection and tracking of maneuvering targets, and thus has unique advantages in covert operation, immunity from various electronic countermeasures，and low cost compared with conventional active radars.
PU-NLMS algorithms can reduce the computational complexity while yield approximate results with their fully-updating counterparts in terms of convergence rate and mean-squared error (MSE)

4. 2 1 INTRODUCTION SIGNAL MODEL 3 2D-PU-NLMS ALGORITHM 4
REAL-DATA EXPERIMENT 5
CONCLUSION

5. 2. SIGNAL MODEL Reference signal Received signal CAF: Noise
the time delay and Doppler of a target in the PBR are extracted by examining the peaks of the CAF
between the reference signal and the residual signal that contains the desired echo signal after clutter cancelation.

6. Diffcult to be cancelled
2. SIGNAL MODEL
Targets
Noise
Received signal
Direct signal
Sea clutters
1D adaptive filters
Wiener
NLMS
RLS ….
Diffcult to be cancelled
Passive bistatic radars (PBRs) exploit the transmitted signals from non-cooperative illuminators of opportunity to perform detection and tracking of maneuvering targets, and thus has unique advantages in covert operation, immunity from various electronic countermeasures，and low cost compared with conventional active radars.

7. 1
INTRODUCTION 2
SIGNAL MODEL 3
2D-PU-NLMS ALGORITHM 4
REAL-DATA EXPERIMENT 5
CONCLUSION

8. 3. 2D-PU-NLMS ALGORITHM 1D adaptive filter
Multi-channel(2D) adaptive filter
modulate the reference
signal onto the Doppler
dimension
a filter with a wide notch and sharp edges
The added fully-updating counterparts in Doppler dimension induces high computational load

9. 3. 2D-PU-NLMS ALGORITHM Multi-channel(2D) NLMS
Multi-Channel Partial-Update(2D-PU) NLMS
Only select L out of
N coefficients to update

10. Computational complexity
3. 2D-PU-NLMS ALGORITHM
Computational complexity
MC-NLMS
KN complex multiplications are required for calculating one output point, and 2KN+K complex multiplications for updating K weight vectors is needed in MC-NLMS for one single iteration.
2D- Partial-Update (L<N)
2KL+K(N+1) complex multiplications are required in the 2D-PU-NLMS algorithm.
MC-NLMS
2D-PU-NLMS
3KN+K
2KL+KN+K

11. 1
INTRODUCTION 2
SIGNAL MODEL 3
2D-PU-NLMS ALGORITHM 4
REAL-DATA EXPERIMENT 5
CONCLUSION

12. 4. REAL-DATA EXPERIMENT
Para.
value
The carrier frequency
714 MHz
bandwidth of the DVB
7.56 MHz
baseband sampling rate
10 MHz.
filter length
1000
The modulated frequencies
stepsize
It can be observed that the 2D-PU-NLMS has approximate results with the fully-updating counterparts in terms of convergence rate and mean-squared error (MSE)
an experimental digital video broadcast (DVB)-based PBRwas installed on the roof of a building located in an eastern coastal city of China, Yantai.
Learning curves for NLMS algorithm, MC-NLMS algorithm and 2D-PU-NLMS
But little performance loss can achieve satisfactory performance in terms of the convergence rate and stability
with a reduced computational complexity using fewer updated points.

13. 4. REAL-DATA EXPERIMENT
CAF results using NLMS algorithm
MC-NLMS
2D-PU-NLMS
N=1000
L=600
L=500
L=300
SCNR(dB)
14.92
14.69
14.53
14.31
The desirable capability in clutter suppression as well
as the reduced computational complexity can be both achieved
by utilizing 2D-PU-NLMS algorithm
CAF results using 2D-PU-NLMS algorithm with L=500

14. 1
INTRODUCTION 2
SIGNAL MODEL 3
2D-PU-NLMS ALGORITHM 4
REAL-DATA EXPERIMENT 5
CONCLUSION

15. 5. CONCLUSION
a novel 2D-PU-NLMS algorithm is proposed, which is derived by extending the NLMS filter to a 2-D structure that accounts for both time delay and Doppler frequency.
Compared with the state-of-the-art MC-NLMS filter, the proposed 2D-PU-NLMS is able to reduce the computational complexity while yields approximate interference cancelation performance in terms of convergence rate and MSE.
The effectiveness of the proposed algorithm is verified using real radar data.