Journal of Electrical and Computer Engineering

Volume 2015, Article ID 864508, 7 pages

http://dx.doi.org/10.1155/2015/864508

## Micro-Doppler Ambiguity Resolution Based on Short-Time Compressed Sensing

School of Information Science and Engineering, Xiamen University, Xiamen 361005, China

Received 4 May 2015; Revised 5 August 2015; Accepted 5 August 2015

Academic Editor: Igor Djurović

Copyright © 2015 Jing-bo Zhuang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

When using a long range radar (LRR) to track a target with micromotion, the micro-Doppler embodied in the radar echoes may suffer from ambiguity problem. In this paper, we propose a novel method based on compressed sensing (CS) to solve micro-Doppler ambiguity. According to the RIP requirement, a sparse probing pulse train with its transmitting time random is designed. After matched filtering, the slow-time echo signals of the micromotion target can be viewed as randomly sparse sampling of Doppler spectrum. Select several successive pulses to form a short-time window and the CS sensing matrix can be built according to the time stamps of these pulses. Then performing Orthogonal Matching Pursuit (OMP), the unambiguous micro-Doppler spectrum can be obtained. The proposed algorithm is verified using the echo signals generated according to the theoretical model and the signals with micro-Doppler signature produced using the commercial electromagnetic simulation software FEKO.

#### 1. Introduction

Estimating and extracting micromotion information have attracted much attention in recent years [1–6]. Micromotion can be defined as the mechanical vibration, rotation, or other higher order motion components, excluding translational motion, of a target and will produce a frequency modulation on the returned signal that generates sidebands about the target’s Doppler frequency. This is known as the micro-Doppler effect. This effect reflects the unique dynamic and structural characteristics of the target, which offers an approach for the recognition and identification of specific targets [7].

In Synthetic Aperture Radar (SAR)/Inverse Synthetic Aperture Radar (ISAR) imagery, micro-Doppler effect will introduce nonstationary phase modulation into returned signals, which will significantly decrease the readability of images [5, 8, 9]. In [8], an estimation method based on the discrete fractional Fourier transform is proposed to estimate the instantaneous vibration accelerations and frequencies. To separate the rigid body and the micro-Doppler parts, an L-statistics-based method for micro-Doppler effects removal is proposed in [9].

Most of the previous researches assume that the probing frequency to a micromotion target is large enough and thus there is no Doppler ambiguity and micro-Doppler ambiguity. However, a long range radar usually works in low PRF, which causes serious Doppler ambiguity and micro-Doppler ambiguity. In [10, 11], a CS-based Doppler ambiguity resolution method is proposed. However, this method cannot be applied to resolve micro-Doppler ambiguities. Therefore, it is necessary to study how to extract the unambiguous micro-Doppler time-frequency spectrum when the PRF is low. In [12], the CS is employed to remove undesirable cross terms in the Wigner-Ville distribution of the micro-Doppler radar signature. However, the micro-Doppler ambiguity problem is not discussed.

To solve micro-Doppler ambiguity problem, we propose a novel method based on CS in this paper. According to the RIP requirement of the sensing matrix, a sparse pulse train with random time stamps is designed based on the fixed-PRF pulses. The echo signals after matched filtering can be viewed as randomly sparse sampling of the micro-Doppler spectrum. To reconstruct the micro-Doppler signature from the sparse samples, we propose a short-time-compressed-sensing time-frequency analysis method. A short-time window slides along the slow-time domain echo signals and the reconstruction algorithm OMP is applied within this window to reconstruct the micro-Doppler spectrum. Two kinds of echo signals, one generated according to the theoretical model and one produced by the commercial electromagnetic simulation software FEKO, are used to verify the proposed algorithm.

#### 2. Theory

##### 2.1. Radar Echo Signal Model

The transmitted chirp signal can be modeled aswhere ; and are the center frequency and the chirp rate, respectively; and are the pulse width and fast time, respectively; is the full time, where denotes the slow time. The received echo signal can be defined aswhere is the backscattered field amplitude for the point scatter, is the speed of electromagnetic wave propagation, and is the instantaneous distance between the radar and the target. Performing matched filtering to the received signal and transforming the results into range-frequency domain yieldwhere is the signal bandwidth. Ignoring the acceleration, jerk of the target, can be approximated bywhere is the distance between the target and radar at time , is the radial velocity, and is the micromotion of the target. Substituting (4) into (3) yields the slow-time domain echo signal:where and is the Doppler frequency and can be written aswhich consists of two parts, that is, the Doppler frequency caused by translational motion and the micro-Doppler frequency caused by micromotion.

##### 2.2. Requirements of the PRF

According to the Nyquist-Shannon sampling theorem, for a band-limited baseband signal the sampling rate must be greater than or equal to two times the highest frequency of the signal. For a pulsed radar, the Doppler frequency of the target is sampled with the sampling rate as the PRF. When the micro-Doppler frequency exceeds half of the PRF, the micro-Doppler ambiguity phenomenon will occur. Thus, the PRF must be greater than or equal to the highest micro-Doppler frequency shift.

In a short-time interval, can be seen as a constant. If there are scatterers each with different micro-Doppler frequencies, the echo signal can be rewritten asIf the PRF is smaller than , the spectrum aliases will occur in (7). According to the sub-Nyquist sampling theorem [13] or CS theory, the sparse signal can be sampled at a rate lower than the Nyquist. The slow-time domain radar echo signal can be viewed as the scalar sum of sinusoidal signals, which is sparse in frequency domain [14]. Thus, the requirement for PRF can be loosened. Next we briefly review the compressing sensing theory.

##### 2.3. Compressed Sensing

The theory of compressed sensing shows that when the signal is sparse or compressible, the signal can be reconstructed accurately or approximately by gathering very few projective values of the signal. Suppose is a -sparse (has nonzero values) or compressive signal after orthogonal mapping projection and is the sparse representation of ; then a measurement matrix , which is irrelevant with the orthogonal map , could be built to measure linearly, resulting in only measured values :

The dimension of is less than that of , so the equation has infinity solutions. By solving the optimal problem above, signal can be approximately reconstructed. is sparse in domain; that is,Substituting (9) into (8) yieldswhere is called sensing matrix with dimension. As long as satisfies the Restricted Isometry Property (RIP), the sparse signal could be recovered from the measured values .

Signal reconstruction is the process of recovering from the linear measured values . The simplest method is to solve the norm:It is optimal in theory but impractical in numerical computation, belonging to a nondeterministic polynomial (NP) hard problem. Donoho and Elad proved that norm and norm minimizations are equivalent if the solution is sufficiently sparse [15]. normis an optimization problem and could be solved using linear programming.

According to the description of Section 2.2, we know that the radar echo signals can be viewed as the scalar sum of sinusoidal signals and are sparse in frequency domain; thus, the compressed sensing can be utilized. The linear measurement is a column vector, which is randomly extracted from the measurements of (7) with the corresponding random sampling time . Since is sparse in frequency domain, the Fourier basis is chosen as the basis matrix with its element defined byExtracting rows corresponding to the random transmitted time from the identity matrix yields the measurement matrix . Thus, the sensing matrix is obtained by randomly extracting row from the sparse matrix and meets the RIP property.

Generally, the value of is related to the relevancy between the sparse matrix and the measurement matrix and satisfieswhere is a constant and is defined as [16]

The classical recovery algorithms of compressed sensing are Basis Pursuit and Greedy Matching Pursuit. BP algorithm is the global optimization algorithm which has several advantages including superresolution and stability, but it is also accompanied with high computational complexity. Greedy Matching Pursuit algorithm, such as Orthogonal Matching Pursuit (OMP), is a local optimization algorithm and has the low computational complexity and high level of localization accuracy. The characteristics of OMP, such as easy implementation and fast speed, make it a better choice than BP algorithm [16].

#### 3. Short-Time Compressed Sensing

Doppler frequency shifts generally have the time-varying characteristic and should be analyzed via the joint time-frequency analysis technique. However, when the PRF is lower than the Nyquist, the traditional time-frequency analysis methods are invalid. According to the above theoretical analysis results and the time-variant properties of micro-Doppler, we design a window-weighted compressed sensing method. According to the RIP of the CS sensing matrix, a sparse pulse train is randomly extracted from traditional fixed repetition frequency pulses. By adding a random perturbation item to the transmitting time of each selected pulse, we can obtain a new transmitting time sequence, which is equivalent to those extracted from a high PRF pulse transmitting time set. Then the corresponding measurement matrix can be obtained according to the transmitting time sequence. After matched filtering to the radar echoes, a short-time window slides along the slow-time domain echo signals and the CS method is applied within this window to reconstruct the micro-Doppler spectrum. Similar to the short-time Fourier transform (STFT), we call this method as short-time compressed sensing.

The PRF of a LRR is usually low. For a LRR working in fixed PRF mode, the slow-time domain echoes suffer from serious micro-Doppler ambiguity. By modifying the th pulse transmitting time to , where is a random perturbation, we can obtain a new transmitting time sequence. Since is random, the equivalent PRF for is larger than that for . The unambiguous micro-Doppler frequency range for will be larger than that for since higher PRF means wider unambiguous micro-Doppler range.

When the value of meets (14), the designed sensing matrix can satisfy the RIP property and the slow-time echo signals can be reconstructed based on the sparse measurements. Therefore, we can reduce the number of transmitted pulses by randomly selecting pulses from the traditional fixed-PRF transmitted time sequences and adding a random perturbation to them. The building of sparse and random transmitted time sequences is demonstrated in Figure 1.