Ronald Mucci

A comparison of efficient beamforming algorithms

Time domain and frequency domain concepts which aid in the design and efficient implementation of a digital beamformer have been described at various times in the literature. The numerous beam-former structures that result are discussed with an emphasis on hardware requirements and spectral areas of application. Time domain procedures which include delay-sum, partial-sum, interpolation and shifted-sideband beamforming, and frequency domain techniques which include the application of discrete Fourier transforms and phase shift beam-forming are considered. Hardware considerations are primarily in the areas of analog-to-digital conversion, data storage, and computational throughput requirements.

Shifted sideband beamformer

A fundamentally different time domain beamformer structure is described which can be used to process bandpass sensor signals efficiently. The beamformer operates directly on complex, frequency translated, single sideband representations of the input signals to obtain a similar representation of the beam output. Such representations are typically obtained by complex demodulation of the signals to facilitate the use of bandwidth sampling procedures. This new technique, which is referred to as the shifted sideband beamformer, is functionally a time-domain beamformer but it combines attributes of both time-domain and frequency-domain beamforming. Shifted sideband beam-forming has the advantage that beamformer vernier delay and throughput requirements depend on the frequency content of the translated band rather than of the original band. This paper discusses the potential hardware savings associated with shifted sideband beamforming in terms of analog to digital conversion, cable bandwidth, digital processing and, also, signal conditioning hardware. The impact of delay quantization on beam-pattern structure is compared for a shifted sideband and a conventional digital implementation. Beamformer throughput is also analyzed for both implementations. A further reduction in the beamformer throughput requirement is demonstrated by the use of digital interpolation in conjunction with the shifted sideband beam-forming concept.

Target State Estimation in a Multitarget Environment Using Multiple Sensors

An approach for target detection/state estimation that views theproblem as one of a finite state search over the target parameterspace is presented. This approach allows for a natural way toassociate different types of measurements, such as frequency andcoherence from multiple sensors, and also for dealing with multipletargets, dropouts, and clutter. We describe our model and present acomputationally efficient search algorithm for target detection andtarget state estimation in a multitarget environment based on thismodel. The results of a two-sensor, multitarget computer simulationare discussed.

A New Approach to Target Detection and Estimation in Clutter

We describe an approach for target detection/state estimation that views the problem as one of a finite state search. This approach allows for a natural way to associate measurements and also for dealing with data from heterogeneous sources. We describe our model and a scoring function based on the model. A search algorithm for target detection and target state estimation is presented, followed by an example.

An efficient procedure for broadband Doppler compensation

Frequently, it is necessary to compress or expand a function by scaling independent variables relating to time and/or distance. For example, the Doppler phenomena, caused by a change in the position of the source of a waveform measured radially from the position where the waveform is received, affects the time scale of the received waveform. For applications that do not satisfy the narrowband criterion, a procedure other than frequency translation is needed to produce a scaling of the independent variable of time to compensate for the Doppler effect. An approach, intended for discrete time applications, involves the two-step process of interpolation and decimation. The proper scaling factor is obtained by interpolation by an integer factor L and decimating, i.e., decreasing the sampling rate, by an integer factor K. The computationally efficient procedure which uses FIR filters is described: the corresponding expressions are derived for the interpolation errors associated with this procedure. Also, modifications to this procedure are described. One such modification is used to accommodate time varying scale factors; another incorporates frequency translations to accommodate uncertainties in the scale factor.