Goal: Target detection in the presence of sea or foliage clutter.

Challenges:

• Radar cross section (RCS) of complex targets are quickly changing functions of the angle aspect, causing signal fading and deteriorating the radar performance.
• Distribution of the real clutter often deviates from Gaussian distribution (and hence deviates from Rayleigh distribution, amplitude of the Gaussian distributed clutter), see Fig.1. The empirical probability density function (pdf) of the clutter magnitude has a " heavy tailed" distribution. The heavy tail can cause high probability of false alarm if Gaussian distributed clutter is assumed in detector design.

Motivation:

• Employing multi-input multi-output (MIMO) radar with widely separated antennas provides spatial diversity and mitigates the target scintillation [1].
• Using compound-Gaussian model for heavy tailed distribution fits the high-resolution and/or low grazing angle radar in the presence of sea or foliage clutter [4].

Our Research:

• Presented a measurement model for a MIMO radar with widely separated antennas in GMANOVA framework.
• Computed the maximum likelihood estimates of the unknown target and clutter parameters assuming a compound-Gaussian clutter model with inverse gamma texture.
• Developed a generalized likelihood ratio test to detect the presence of a target.
• Showed the advantage of MIMO radar over conventional phased array radar for target detection (Fig. 2(a)).
• Demonstrated the further improvement in the detection performance due to the proposed adaptive energy allocation algorithm which allocates the transmitted power among the transmitting antennas by maximizing the probability of detection (Fig.2(b)).

References:

1. J. Li and P. Stoica, MIMO Radar Signal Processing. Wiley-IEEE Press, Oct. 2008.
2. P. Lombardo, and M. Greco, Radar clutter modeling, Tutorial in IEEE 2005 International Radar Conference, Washington DC, May 9-12, 2005.
3. A. Balleri, A. Nehorai, and J. Wang, "Maximum likelihood estimation of compound-Gaussian clutter with inverse gamma texture," IEEE Trans. on Aerospace and Electronic Systems, Vol. 43, pp. 775-779, Apr. 2007.
4. J. Wang, A. Dogandzic, and A. Nehorai, "Maximum likelihood estimation of compound-Gaussian clutter and target parameters," IEEE Trans. Signal Process. vol. 54, no.10, pp. 3884-3898, Oct. 2006.
5. M. Akcakaya and A. Nehorai, "Adaptive MIMO radar design and detection in compound-Gaussian clutter," IEEE Trans. on Aerospace and Electronic Systems, vol. 47, no. 3, pp. 3241-3250, Jul 2011.
6. M. Akcakaya and A. Nehorai, "MIMO radar detection and adaptive design in compound-Gaussian clutter," Proc. IEEE Int. Radar Conf., Washington DC, USA, May 10-14, 2010, pp 236-241.
7. M. Akcakaya, M. Hurtado and A. Nehorai, \MIMO radar detection of targets in compound-Gaussian clutter," Proc. 42nd Asilomar Conf. Signals, Syst. Comput., Pacific Grove, CA, USA, Oct. 2008, pp. 208-212.