Description:

OBJECTIVE: The objective of this topic is to design waveforms that optimize extraction of radar target signals in noise and high clutter environments and discrimination of targets. The desire is to detect radar signals at much lower signal to noise+clutter (SNC) ratios and discriminate the targets based on state information and their structure in the range Doppler matrix. Image processing techniques should be considered to extract signals from the range Doppler matrix. DESCRIPTION: Pulse compression in radar signal processing has been a standardized process for many years [1]. The basis of the standard pulse compression is the matched filter (MF) and the matched filter is the optimal filter to maximize the signal to noise ratio (SNR) given that the signal of interest is corrupted only by additive Gaussian white noise [2]. The matched filter is an excellent technique that is robust, reliable, and is easy to implement. Unfortunately, many times the signal of interest is no longer noise limited, but is instead clutter limited. The metric of interest is no longer the SNR, but it is now the signal to clutter ratio (SCR) and the MF is no longer the optimal technique. The study of how to reduce the SCR is an open problem with no universal solution. Optimal solutions have been found for certain waveforms that maximize SCR while minimizing the mismatch loss (or the degradation in SNR compared to the MF). Examples of such filters for a 13-element Barker Code are given in the literature [4]. Other more sophisticated phase coded wave forms are a possibility. MTI filters and MTD filters also exist, but may not be applicable if both targets are moving at similar speeds relative to the radar (or are stationary) [2]. Recently, researchers have proposed iterative techniques to solve the maximization of SCR on a range cell to range cell basis using results from machine learning, compressive sampling and adaptive signal processing [5] [10]. Results of the methods from [5] and [7] when applied to the same show that these new receive filters reduced the clutter such that target and communication signals can be extracted in high clutter environments. The cost of the techniques in [5] - [10] compared to standard pulse compression techniques is not limited to a minor loss in SNR. There is also a significant cost increase in computational complexity. The matched filter can be written as a scaled inner product of the received signal and the transmitted signal. An inner product has a computational complexity of O(N) (N+1 multiplications and N-1 adds where N is the length of the phase coded waveform) which can trivially be attained by any modern computer. The algorithms from [5] [10] have complexities that are on the order of O(L*N3) (where L is the number of range cells to be processed ). We can usually assume that L>>N which implies that our computational complexity is greater than 4th order polynomial time. As N increases (this is desired in a radar system as it provides more energy on the target) our computational complexity soon overcomes the capacity of most standard computer systems. The intent of this effort is to determine the most effective wave forms and signal processing methods. Follow on efforts will determine are the computationally efficiency of the waveforms and signal processing. PHASE I: The offeror shall analytically design and test a group of algorithms that are optimum for SCR reduction in different environments The government will furnish a terrain clutter model to test the algorithms. The offeror shall design the signal processing hardware and software for processing these waveforms and estimate costs and run times to execute the algorithms. PHASE II: The offeror shall develop a software design for implementation of the waveform simulation and execution on parallel computer architectures. The offeror shall develop a detailed simulation of the waveforms, signal processing, and hardware architecture. The offeror shall develop a computer hardware architecture and demonstrate execution of simulated signal returns using the proposed architecture. The offeror shall document the waveform designs, hardware architecture design, and test results in a final report. PHASE III: The results of Phase II shall be presented to Government and industry for commercialization of the waveforms in future radar/ communication systems. The products for commercialization are the hardware architecture, waveform designs, signal processing algorithms and software parallelized to optimize processing on the hardware architecture. REFERENCES: [1] E. Farnett and G. Stevens,"Pulse Compression Radar,"in Radar Handbook, 2nd edition, M. Skolnik, Ed, Boston, MA, McGraw-Hill, 1990. [2] N. Leavnon and E. Mozeson, Radar Signals. Hoboken, NJ, Wiley, 2004. [3] P. Stoica, J. Li, M. Xue,"On Binary Probing Signals and Instrumental Variables Receivers for Radar,"IEEE Trans. Inf. Theory, vol. 54, no. 8, pp. 3820-3825, Aug. 2008. [4] M. Ackroyd, F. Ghani,"Optimum Mismatched Filters for Sidelobe Suppression,"IEEE Trans. Aerosp. Electron. Syst. Vol AES-9, no. 2, pp 214-218, Mar. 1973. [5] S. Blunt and K. Gerlach,"Adaptive Pulse Compresssion"IEEE Trans. Aerosp. Electron. Syst. Vol. 42, no. 2, pp 572-584, Apr 2006. [6] S. Blunt, K. Gerlach, K. Smith,"Doppler-Compensated Adaptive Pulse Compression,"in Proceedings of the IEEE Conference on Radar, Verona, NY, Apr. 24-27, 2006, 114-119. [7] T. Yardibi, et al."Source Localization and Sensing: A Nonparametric Iterative Adaptive Approach Based on Weighted Least Squares,"IEEE Trans. Aerosp. Electron. Syst. Vol. 46, no. 1, pp 425-440, Jan. 2010 [8] X. Tan, et al"Range-Doppler Imaging Via a Train of Probing Pulses"IEEE Trans. Signal Processing, vol. 57, no. 3, pp 1084-1097, Mar. 2009. [9] X. Tan, et al"Sparse Learning via Iterative Minimization with Application to MIMO Radar Imaging"IEEE Trans. Signal Processing, vol. 59, no. 3, pp 1088-1101, Mar. 2011. [10] S. Cotter, et al"Sparse Solutions to Linear Inverse Problems with Multiple Measurement Vectors"IEEE Trans. Signal Processing, vol. 53, no. 7 July 2005.