Target Impulse Response Research Articles

Radar target identification using the bispectrum: a comparative study

Radar target identification is performed using time-domain bispectral features. The classification performance is compared with the performance of other classifiers that use either the impulse response or frequency domain response of the unknown target. The classification algorithms developed here are based on the spectral or the bispectral energy of the received backscatter signal. Classification results are obtained using simulated radar returns derived from measured scattering data from real radar targets. The performance of classifiers in the presence of additive Gaussian (colored or white), exponential noise, and Weibull noise are considered, along with cases where the azimuth position of the target is unknown. Finally, the effect on classification performance of responses horn extraneous point scatterers is investigated. >

Information theory and radar waveform design

The use of information theory to design waveforms for the measurement of extended radar targets exhibiting resonance phenomena is investigated. The target impulse response is introduced to model target scattering behavior. Two radar waveform design problems with constraints on waveform energy and duration are then solved. In the first, a deterministic target impulse response is used to design waveform/receiver-filter pairs for the optimal detection of extended targets in additive noise. In the second, a random target impulse response is used to design waveforms that maximize the mutual information between a target ensemble and the received signal in additive Gaussian noise. The two solutions are contrasted to show the difference between the characteristics of waveforms for extended target detection and information extraction. The optimal target detection solution places as much energy as possible in the largest target scattering mode under the imposed constraints on waveform duration and energy. The optimal information extraction solution distributes the energy among the target scattering modes in order to maximize the mutual information between the target ensemble and the received radar waveform. >

Principles of high-resolution radar based on nonsinusoidal waves. III. Radar-target reflectivity model

For pt.II see ibid., vol.31, no.4, p.369-75 (1989). A target-reflectivity model is developed for carrier-free nonsinusoidal waves with the time variation of a Gaussian pulse or a sequence of positive and negative Gaussian pulses representing a binary code, as was introduced in pt.I (see ibid., vol.31, no.4, p.359-68 (1989)). It is shown that the impulse response of a complex target that is composed of a finite number of scattering centers can be expressed as a sequence of Gaussian pulses. The characteristics of the Gaussian pulses, e.g. peak amplitude and nominal duration, are functions of the physical properties of the scattering centers, which are unique for each target. Hence, the impulse response waveform of a target can be regarded as a one-dimensional image in time (or range), which is valuable information for target classification and recognition. A signal processing technique is developed for obtaining an approximation of a target impulse response waveform from the backscattered and received signals. The signal processor is specifically developed for radar signals with the structure of complementary code pairs whose autocorrelation function is a single narrow pulse with no time sidelobes. >

Space-frequency sampling criteria for electromagnetic scattering of a finite object

The sampling criteria in the wavenumber space for generating the spatial impulse response of a finite target is described. A proper choice of canonical confinement for the target in space can greatly reduce the number of samples required to sufficiently characterize the target's spatial impulse response. Though a sampling lattice may be more efficient in the sense of a reduced number of measurement points per unit volume of hyperspace, it may be less effective when digital processing is involved. Specifically, the time-consuming interpolating step is required to put data presented in other types of sampling lattice into the proper type for the computer. Two-dimensional impulse responses reconstructed from cubic sampled data are compared with those using Mensa et al.'s method. The responses obtained also indicate good potential for image reconstruction via the spatial impulse response.

Digital SAR processing using a fast polynomial transform

In this paper, a new digital processing algorithm is developed for producing images from SEASAT synthetic aperture radar (SAR) data. For reasons of ecomony, SEASAT presently operates in the stripmapping mode rather than the potentially higher resolution spotlight mode [1]. This algorithm applies the fast polynomial transform (FPT) for computing the two-dimensional cyclic correlation of the raw echo data with the impulse response of a point target. This two-dimensional correlation algorithm solves the range migration problem. It is demonstrated that this SAR processing technique is readily implemented on a general purpose computer. Actual results of SEASAT SAR imagery are given in this paper.

Two methods to deconvolve: L<inf>1</inf>-method using simplex algorithm and L<inf>2</inf>-method using least squares and parameter

If r(t) is the linear scattering response of an object to an excitation waveform e(t) , then r(t) = (e \ast h) (t) . One would like to deconvolve and solve for h(t) , the impulse response. It is well-known that this is often an ill-conditioned problem. Two methods are discussed. The first method replaces the discretized matrix form E \cdot H = R by the following problem. Minimize \|h_{1}\|+ \ldots + \|h_{n}\| subject to R - \lambda \leq E \cdot H \leq R + \lambda where \lambda is a column vector chosen sufficiently small to yield acceptable residuals, yet large enough to make the problem well-conditioned. This problem is converted to a linear programming problem so that the simplex algorithm can be used. The second method is to minimize \parallel E \cdot H - R \parallel^{2} +\lambda \parallel H \parallel^{2} where again \lambda is chosen small enough to yield acceptable residuals and large enough to make the problem well-conditioned. The method will be demonstrated with a Hilbert matrix inversion problem, and also by the deconvolution of the impulse response of a simple target from measured data.

The Hydroshock® water gun

An impulsive acoustic source has been developed which uses water under high pressure to create a controlled, impulsive signature generally free of bubble pulses or other aftershot clutter. The gun uses high-pressure water to force a piston back against an air spring to a stop position. A trigger signal releases the piston which drives water through ports in the gun housing to form jets at right angles to the axis of piston travel. The piston abruptly closes the ports as it passes by them, terminating the jets and generating cavitation voids. The voids subsequently collapse in synchronism, away from the body of the gun, creating the gun signature. A prototype model, designated HS-56, exhibits a peak-to-peak signature in excess of 8 bar meters (238 dB re 1 μPa@ 1 M) in a 0–2 kHz band, and can be fired at repetition periods of less than 4 s with a 15-hp pump. This model appears useful for high resolution engineering profiling, subbottom investigations, target impulse response studies, and various signaling applications. The gun concept and the results of calibration, reliability and operational tests will be described. A comparison of HS-56 performance with that of competing seismic sources will also be discussed.

Analysis of target recognition via echolocation by an Atlantic bottlenose porpoise (Tursiops truncatus)

The targets used in the “Target recognition via echolocation by an Atlantic bottlenose porpoise (Tursiops truncatus)” study were acoustically examined in order to determine the salient features of the targets that would allow the animal to recognize and differentiate between the different targets. The echoes from the targets were obtained by using a broadband dolphinlike echolocation signal that had a similar waveform, peak frequency, and bandwidth with measured echolocation signals. The waveshape, frequency spectrum, target impulse response, and the matched‐filter response of the echoes were analyzed to determine features of the echoes that could be used by the animal to discriminate between targets. The echoes usually consisted of the major specular reflection for the front of the target followed by echoes of progressively diminishing amplitudes for the exterior of the target. The first two echo components, from the front face and interior boundary of the rear face, had the predominant influence of the shape of the spectrum. The results of the matched‐filter analysis showed a close correspondence with the animal's performance.

Statistical dependence of subpulse echoes in active sonar systems using frequency diversity

Active sonar systems using waveforms consisting of M subpulses of different frequencies are a common method for counteracting the Rayleigh fading statistics of a single pulse echo. When frequency differences between adjacent subpulses are not large in comparison with the reciprocal of target-impulse response durations, the various subpulse echoes are not statistically independent. This lack of independence reduces the 2M statistical df that are associated with statistically independent echoes. Probability density functions of squared and summed subpulse echoes plus reverberation and noise are determined as functions of target-impulse response durations and subpulse frequency spacings. Limiting cases are shown to coincide with the classical chi-square distribution results. Effects of time constants of reverberation and noise-estimation circuits on receiver operating characteristics also will be discussed.

Sonar for generalized target description and its similarity to animal echolocation systems

Range‐distributed targets are often characterized arrays of point scatterers. This conceptualization corresponds to a target impulse response that is a sequence of impulses. A physically meaningful generalization of this description is obtained by modeling an impulse response in terms of pulse doublets, impulses, step functions, ramp functions, etc., i.e., as a superposition of delayed impulses and differentiated or integrated impulses. The resulting impulse response model resembles a spline function without continuity conditions. A sonar system for generalized target characterization is described; signals and receivers are specified for (1) optimum estimation of reflector parameters and (2) detection of a known target in clutter. The derived signals correspond closely with some of the waveforms that are used by dolphins and bats for echolocation. The corresponding echo analyzers are similar to some psychoacoustic models of the mammalian hearing system.