Autoregressive moving average modeling of radar target signatures

Abstract

A method for characterizing radar target signatures with Autoregressive Moving Average (ARMA) models is developed. A parameterization of the model that corresponds directly to the geometric properties of the target is chosen, and an efficient algorithm for estimating these parameters is presented. Procedures for minimizing the effects of unmodeled dynamics are also developed. Experiments on radar measurements obtained from a compact range are presented to test the effectiveness of the ARMA modeling procedure.

Effectiveness of Moving Signal‐Average Method in K Band FMCW Radar for Short‐Range Vehicle Detection Using Antennas With Narrow Beam Widths

“…The MSAM (Carriere & Moses, ; Vadakkoot et al, ) averages the echo signals within a time window that is employed to extract the data from the FFT peak value distribution. The time window is progressively shifted by a time step, Δ t (or a location step, Δ d ), to extract another set of data for MSAM processing.…”

“…The basic concept is to collect sufficient echo signals scattered from the moving TUD at a period of time during which every portion of TUD is well illuminated by the incident field of antenna radiation. The averaged signal found by using moving signal‐average methods (MSAMs) (Carriere & Moses, ; Vadakkoot, Shah, & Shriva, ) may decrease the mistakes of instant detection determination when the electromagnetic (EM) fields illuminate only parts of the TUD. In the practical application of continuous signal reception, depending on the planned separation distance between vehicles (the safe distance of driving in terms of speed), a time window frame is employed to collect the echo signals and average them by using the proposed MSAM schemes.…”

Target Classification With Low-Resolution Radars Based on Multifractal Correlation Characteristics in Fractional Fourier Domain

“…In this part, we experimentally study the performance of MCIFD method for aircraft target classification and recognition with the measured data. The experimental results show that the radar target correct classification rates (CCRs) based on multifractal features in time domain were higher than that of dispersion situations of eigenvalue spectra and amplitude modulation feature and frequency domain entropy [7][8][9][10]. Ref.…”

“…In view of the difficulty in classifying and identifying aircraft targets with conventional low-resolution radars, many researchers have explored and obtained certain research results. Existing features for aircraft target classification include: Waveform characteristics of aircraft target echoes, amplitude modulation features, entropy in the frequency domain, ARMA (Autoregressive moving average model), dispersion situations of eigenvalue spectra, EEMD (Ensemble empirical mode decomposition), and jet engine modulation (JEM) features, among which JEM features account for the majority [6][7][8][9][10][11]. Ref.…”

Adaptive multichannel sequential lattice prediction filtering method for ARMA spectrum estimation in subbands

“…Here, x(n) is zero mean, white noise with a variance of σ 2 x , andâ 1 andâ 2 j , respectively, represent the th and jth coefficients related to AR and MA parts. Such processes arise in various applications such as modeling radar signals [4,5] or speech signals [6,7], where spectral zeros as well as poles are often present due to the physical mechanism generating the data. In addition, processes that are purely autoregressive are often transformed into ARMA(p, p) processes by addition of measurement noise, and especially sinusoids in noise are known to obey the degenerate ARMA equation [8,9].…”