Direction of arrival estimation using advanced signal processing

Accurate estimation of signal direction of arrival (DOA) has many applications in communication and radar systems. For example, in defense application, it is important to identify the direction of possible threat. One example of commercial application is to identify the direction of emergency cell phone call such that the rescue team can be dispatched to the proper location. DOA estimation using a fixed antenna has many limitations. Its resolution is limited by the mainlobe beamwidth of the antenna. Antenna mainlobe beamwidth is inversely proportional to its physical size. Improving the accuracy of angle measurement by increasing the physical aperture of the receiving antenna is not always a good option. Certain systems such as a missile seeker or aircraft antenna have physical size limitations; therefore, they have relatively wide mainlobe beamwidth. Consequently, the resolution is quite poor. Also, if there are multiple signals falling in the antenna mainlobe, it will be difficult to distinguish them. Instead of using a fixed antenna, an array antenna system with innovative signal processing would enhance the resolution of signal DOA. It also has the ability to identify multiple targets. Two types of signal processing methods, model based and eigen-analysis estimation techniques, are presented in this paper. The model based approach models the observed data as the output of a linear shift invariant system driven by zero mean white noise. The signal's DOA can be estimated by evaluating the model parameters. This approach has properties similar to the maximum entropy spectrum estimation [1]. Some of the problems in the maximum entropy method, such as the line splitting effect, are also observed in this method. Two different processing algorithms are used to obtain the model parameters, and they are: 1. Least Mean Square (LMS) and 2. Sample Matrix Inversion (SMI). The eigen-analysis method based on temporal averaging has been investigated by many authors in the past [2]. However, temporal averaging requires average over multiple time samples to estimate the covariance matrix. Sometimes, the radar system prefers to have an estimated covariance in a single snapshot. We propose eigen-analysis based on spatial smoothing so that we can have estimated covariance in a single snapshot. Performances based on several different spatial averages are discussed in this paper. Extensive computer simulations are used to verify the processing algorithms. For narrowband signals, both processing algorithms provide enhanced resolution and have ability to resolve multiple targets as long as the number of targets is less than the system's degree of freedom. SMI provides better performance than the LMS method due to the fact that this method is relatively immune to excessive mean square error. However, for multiple wideband waveforms, sometimes the array antenna has difficulty to resolve them, especially if signals are impinging the antenna with narrow spatial separation. This problem can be solved by extending the array antenna to a space time adaptive processor (STAY) [3]. STAP is basically replacing the single weight at the output of each array element by an adaptive filter. Statistical analysis of the performance of the processing algorithms and processing resource requirements are discussed in this paper.