http://iet.metastore.ingenta.com
1887

Motion compensation for TDM MIMO radar by sparse reconstruction

Motion compensation for TDM MIMO radar by sparse reconstruction

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
Electronics Letters — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

A motion-compensation method that applies sparse reconstruction (SR) to reconstruct the Doppler spectrum of targets based on a random transmission scheme is proposed for time-division multiplexing (TDM) multiple-input multiple-output (MIMO) radar. Since the random transmission can eliminate the characteristic of periodic time-delay in conventional TDM scheme between transmit cycles, the angle information of a target is not affected by its motion. Therefore, the angle and velocity are no longer coupled with each other and can be estimated separately. This method not only overcome the space–frequency coupling problem but also enhances the unambiguous Doppler interval. Another advantage is that the method is valid even when the estimated target velocity is ambiguous. The results reported here offer the possibility of utilising SR to solve conventional TDM MIMO problems. The effectiveness of the proposed method is demonstrated by experimental results.

References

    1. 1)
    2. 2)
      • C.M. Schmid , R. Feger , C. Pfeffer .
        2. Schmid, C.M., Feger, R., Pfeffer, C., et alMotion compensation and efficient deign for TDMA FMCW radar systems’. The 6th European Conf. on Antennas and Propagation (EUCAP), Prague, 2012, pp. 17461750.
        . The 6th European Conf. on Antennas and Propagation (EUCAP) , 1746 - 1750
    3. 3)
    4. 4)
      • F. Belfiori , W.V. Rossum , P. Hoogeboom .
        4. Belfiori, F., Rossum, W.V., Hoogeboom, P.: ‘Random transmission scheme approach for a FMCW TDMA coherent MIMO radar’. Proc. of IEEE Radar Conf., Atlanta, GA, 2012, pp. 01780183.
        . Proc. of IEEE Radar Conf. , 0178 - 0183
    5. 5)
      • E.J. Candes , T. Tao .
        5. Candes, E.J., Tao, T.: ‘Near-optimal signal recovery from random projections: universal encoding strategies’, Trans. Inf. Theory., 2013, 7, (1), pp. 4754.
        . Trans. Inf. Theory. , 1 , 47 - 54
    6. 6)
      • M. Grant , S. Boyd , Y.Y. Ye .
        6. Grant, M., Boyd, S., Ye, Y.Y.: ‘MATLAB software for disciplined convex programming’, 2007. Available at http://www.stanford.edu/boyd/cvx/V.1.0RC3.
        .
http://iet.metastore.ingenta.com/content/journals/10.1049/el.2017.3524
Loading

Related content

content/journals/10.1049/el.2017.3524
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address