Efficient Detection Algorithm for Non-Coherent High

Efficient Detection Algorithm for Non-Coherent High-Resolution Radar in Sea Clutter Cristina Carmona-Duarte(1), Jaime Calvo-Gallego(2), B. Pablo Dorta-Naranjo(3), Miguel Ángel Ferrer-Ballester(4) (1,3,4)

Instituto Universitario para el Desarrollo Tecnológico y la Innovación en comunicaciones (IDeTIC) University of Las Palmas de Gran Canaria Las Palmas, Spain (1) (3) [email protected], [email protected], (4) [email protected],

Abstract—This paper proposes a new detection algorithm suitable for high-resolution Frequency-Modulated ContinuousWave radars. This algorithm takes into account that the highresolution target echo is composed of several peaks along the range and the new real high-resolution peaks features that are presented in this paper. The performance of the proposed algorithm is evaluated using real and simulated data and compared with other classical detectors. This algorithm is also used for automatic target recognition.

(2)

Department of Computing and Automatics, University of Salamanca Zamora, Spain [email protected]

constant false alarm depends on the clutter level and for highresolution radar, many target reflectors can be undetected due to clutter level.

Keywords— Gaussian clutter, non-Gaussian clutter, noncoherent radar detection, extended targets, automatic target recognition.

I.

INTRODUCTION

With the aid of the new High-Resolution Radars (HRR) it is possible not only to detect but also to identify potential targets [1-6]. In recent years, Frequency-Modulated Continuous-Wave (FMCW) radars [7] have become very popular because of the large advances in high power solid state amplifiers and their Low Probability of Interception (LPI) characteristics. These radars work in K-Band (28 GHz) where it is possible to get good resolution with bandwidths up to 2 GHz and low external system noise influence. There are some differences between radar signals depending on the radar resolution. First, the range profile from target changes with resolution. For low-resolution radar, target information averages only over a few range cells. However, in the case of very HRR with range cells sizes lesser than 0.20 cm, the target is extended over many range cells. In the case of extended targets, the radar signal is composed of several separate amplitude peaks produced by each target reflector [8, 9], as we can see in (Fig. 1). Second, the clutter distribution changes with radar resolution. Phase and quadrature components of clutter echoes in Low-Resolution Radars (LRR) are considered as a Gaussian disturbance and the amplitude distribution as a Rayleigh [10, 11]. These distributions are not adequate for HRR, and the classical Constant False Alarm Rate Detectors (CFAR) cannot support a constant false alarm rate (Pfa) in this new setting. The reason for this is that the threshold required to keep up a

Fig. 1. Range profile of real target (120 m length ferry) and target reflector example.

Classic target detector algorithms adapted to HRR tried to solve these problems with different results, amongst which are the following: • Researches approached the problem modifying the CFAR such as CFAR with Artificial Neural Network (ANNCFAR) [12] and with Switching CFAR (S-CFAR) [13]. • Knowing that when high-resolution target echo is composed of several peaks along the range or the time, detection algorithms of the extended target along the range (from cell to cell) have been developed [14-16]. • Moving targets immersed in sea clutter are usually detected by using speed discrimination by Moving-Target Indicator (MTI), Doppler processors, Track-Before-Detect (TBD), and algorithms based on the application of the Radon transform [17-23]. These algorithms have the disadvantage that arising from techniques used in radar with lower resolution; they have not taken into account advantages of the high-resolution radars. In fact, as resolution increases, less effective are those algorithms.

In this paper, we present a novel algorithm for non-coherent target detection in sea clutter suitable for HRR. This algorithm is based on a new study about HRR signal characteristics. Differences between noise, target signal, clutter and also with different sea states have been shown. Advantages of this algorithm are that it is simpler than previous ones, works with 1-D vector instead of 2-D matrix, it has good performance in noise and clutter environments and it is able to detect nonmoving targets with low signal to clutter ratios. Also with the aid of this algorithm it is possible to obtain automatic profile alignment which results in improved target identification. This paper is organized as follows. In Section II, the k-band Continuous Wave Linear Frequency Modulation (CWLFM) radar scheme and the received signal features are presented. Section III explains the new simple detection algorithm. Performance analysis of the proposed method is compared with CFARs detectors [13, 24-26] as “Cell Averaging” (CA-CFAR), “Smaller Of” (SO-CFAR) and binary integration (for extended target) in Section IV. To confirm that the proposed algorithm is independent with respect to clutter distribution [27] detection performance with Gaussian noise and non-Gaussian model are analyzed. Finally, conclusions are presented in Section V. II.

signals are obtained and then filtered and amplified. The resulting IF signal is captured by a data acquisition card in a PC. A non-coherent signal for a static spot target at a distance r0 from the radar is given by:

⎡ 2πfm r0 ⎤ n ⎥ + nq ( n ) ⎣ f s Δro ⎦

d q ( n ) = A cos ⎢

(1)

where A is the received signal amplitude, fm the modulation frequency, fs the sampling frequency, nq(n) Gaussian noise, r0 the distance to the nearest point of the target reflector (Fig. 1) and Δro is the Radar resolution which depends on the radar VCO (Δfm ) bandwidth and light speed (c) and it is equal to: Δro =

c

(2)

2 Δf m

If we consider a target reflector whose width (L) is larger than the radar resolution with zero radial speed, the IF signals received from the target reflector may be written as:

K-BAND CWLFM RADAR SIGNAL FEATURES

nc ⎡ 2π f m ⎤ d q ( n ) = ∑ Ar cos ⎢ ( ro + lΔro ) n ⎥ + n q ( n ) l =0 Δ f r ⎣ s o ⎦

(3)

Where Ar is the amplitude of each reflector and nc = round ( L / Δro ) is the number of range cells ranged by the target reflector, and rounded to the nearest integer. As the values of Ar are similar, they are approximate to a normalized constant value equal to 1. Realize that the lowest frequency of the signal dq(n) is proportional to the distance from the reflector and its bandwidth is proportional to the peak width. Its Fourier transform Dq (ω ) consists of a sequence of nc deltas at the

discrete

frequencies 2π

f m ( r0 + lΔr0 ) /( f s Δro ), l = 1, 2,..., nc .

Note that the deltas are separated 2π

Fig. 2. Block diagram and photos of the experimental Linear FMCW radar system.

A theoretical study of high-resolution signal is presented in this section which is contrasted with real data. From this study new differences between target, sea-clutter and noise have been addressed. The radar used in this work for database acquisition is a kband homodyne high-resolution Linear-Frequency-Modulated Continuous-Wave (LFMCW) millimeter-wave radar prototype (Fig. 2), already described in [7]. Implementation requires a 400 Hz sweep control signal, 500 MHz bandwidth, and 14.5 GHz central frequency VCO. VCO output is multiplied by 2, to meet a 1 GHz bandwidth (0.15 m range resolution). Once amplified, the signal is transmitted, and when a target is found its echo is captured by the receiver antenna. The received signal is mixed with the transmitted signal. This way the IF

fm / fs

radians. Therefore,

as the number of samples is N = f s / f m , and the applying the Discrete Fourier Transform (DFT) we only gather samples from the discrete points where the signal exist, obtaining a rectangular pulse.. The resulting rectangular pulses position and width are proportional to the distance and length of the target reflector respectively allowing reliable target detection. This fact is characteristic of HRR and it will be used in the algorithm later proposed. Concisely, this signal is obtained applying a Hamming window of N-samples to the IF signal dq(n), then it is synchronized with the VCO sweeper, and finally computing the DFT to the signal. Finally, we obtain: nc Dq (k ) = ∑ δ (k − fm (r0 + lΔr0 ) N /(Δro f s )) + σ n ( k ) l =0

(4)

for 0 ≤ k ≤ N − 1 , which consists of a rectangular pulse of length nc = L / Δro samples. If the Fourier transform of Dq (ω ) is performed, we obtain a Sinc such as:

X (η ) =

sen (πη L / 2 Δro ) sen (πη / 2 )

+σN

for

0 ≤η ≤π

(5)

in which first zero is given at: η =

2 Δro L

(6)

This result is assessed on the particular case of two different hot-spots based on a vessel target analysis (1 and 2 in Fig. 1). The windowed signal is taken with a rectangular window with the same length for all the peaks. The X (η ) obtained is shown in Fig. 3. The same procedure is adopted with the second hot-spot and noise signal.

profiles converge towards a Weibull distribution with shape parameter k= 0.3 and scale parameter λ=0.2 (Fig. 4). In the second case, the experiment was developed with different sea states and different radar angles. From this experiment it is possible see that clutter spike width, observed at grazing angles (it means, the angle between the line-of-sight from the radar to the target and the horizontal in which the target is located) less than 2 degree in sea state 5, converge towards a Weibull distribution with k=0.57 and λ=0.042. These experimental results are shown in Fig. 5, allows for the probability of a false alarm for a given spike width. In Fig. 5, it can also be observed how clutter peaks width distribution change with sea state.

The B and C signals of Fig. 3 which correspond to the two selected vessel hot spots show how the main power is located for η value lower than 0.15. Therefore, as Δro = 0.15 , the width (L) of both hot-spots is over 2 m, as we can calculate with (5). In the case of the noisy A signal of Fig. 3, the power is distributed across the spectrum. So, there is not a clear minimum and it doesn’t correspond to a target. To quantify the differences between the clutter peaks width and target ones in range, the previous study was carried out with a larger real database which contains 400 HRR vessel profiles, different sea states (from 1 to 5), vessel sizes, and distances to radar. This database was captured from two different locations in Gran Canaria (Spain). The first location far away from the sea (“Campus de Tafira” to 5km from “Las Palmas y La Luz” harbour) and the second near the sea (“Instituto de Ciencias Marinas de Taliarte” shortest distance 400 m. from sea). In the second location the sea-clutter database was captured, in different sea-states and different angles with respect to the sea.

Fig. 4. Targets peaks width distribution to a resolution of 15 cm. Weibull distribution k= 0.3 and λ=0.2

From this experimental study it is possible to see differences between clutter and targets by measuring the peaks width. Therefore, new detectors can be developed to exploit these differences as shown in the following section and also it is possible to improve classical detectors.

Fig. 5. Clutter peaks width distribution for different sea states (Douglas Sea Scale). Fig. 3. The distance range Fourier transform, X (η ) .

The experiment carried out consists of measurement spike width first in noise and clutter signal when target was presented, and then only with noise and clutter. In the first case, when target was presented, width distribution in distance

III. DESCRIPTION OF THE PROPOSED ALGORITHM Section II results can be used to develop a new algorithm for target detection whose block diagram is depicted in Fig. 6. It is possible to separate target signal from noise, knowing that

the targets are composed of one or more peaks and these peaks are longer than noise peak width. In the first step of the algorithm, the received signal Dq (k )

is

high-pass

filtered

to

suppress

non-stationary

continuous level. For good performance, normalized low cutoff frequency 0.001 is used.

Fig. 6. Proposed algorithm scheme.

After the high pass filter, the signal is limited by a threshold (T1) (block 2, Fig 6). This threshold is the root-mean-square value (or RMS value) of the system input noise, σn(k), after the high-pass filter. This limiter is used to limit the dynamic range and it avoids high level interferences. In the case of noise signal and based on experimental and simulation results we found that an appropriate value of T1 is given by: T 1opt = 8 ⋅

1 L 2 ∑ σ n( k ) k = 1 L

(7)

to represent target echoes. Monte Carlo simulations have been performed for a fixed probability of false alarm of 0.001. The threshold values were estimated by 105 runs in 2000 signal samples. First, the detector performance in Gaussian noise environment is evaluated, (see Fig. 8) with a small boat. The proposed algorithm performs better than CA-CFAR, SO-CFAR and Binary Integration. In terms of detection probability (Pd) Pd=0.9, the proposed algorithm outperforms CA-CFAR and SO-CFAR detector by 2 dB and the binary integrator by 4 dB. With the “Volcán de Timanfaya” ferry target, see Fig. 9, results are similar except in the Binary Integration case, where the proposed algorithm is 8 dB better. Clutter simulation used a log-normal distribution with σ=0.8 to model the clutter amplitude which corresponds to the 3 Beaufort number, on the Beaufort scale. This clutter distribution is based on [27, 28] that has been shown to work for the actual data obtained in our work. Fig. 10 and Fig. 11 observation reveals that the proposed algorithm performs considerably better than Binary Integration. For Pd=0.9, the presented algorithm requires more than 8 SCR less than the CFAR detectors for the boat case and the ferry case more than 15 dB.

The limited signal is then low-pass filtered with cut-off frequency equal to 0.2. This cut-off frequency is obtained from equation (6) and Fig. 5 to get a Pfa less than (in sea state 2). The cut-off peaks width can be selected with the T2 threshold.

Fig. 7. Range profile before (a) and after (b) filter.

Finally, the filtered signal (D’q(k)) is compared with a threshold T2 (block 4 of Fig. 6), as shown in Fig. 7 (b). If the signal level in a range cell distance is larger than the threshold, it is then considered to be target. However, if it is lower it is noise or clutter. This T2 threshold is different for noise or clutter cases. In the Gaussian noise case, T2 is 0.25. In the clutter case, optimal T2 is given by: T 2 c = 5 ⋅ log(T 1)

IV.

Fig. 8. Proposed, CA-CFAR, SO-CFAR and CA-CFAR detection performance along with binary integrator algorithms for Gaussian noise with small boat. Pfa =0.001.

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PERFORMANCE ANALYSIS

An algorithm performance evaluation is carried out by comparing the proposed detection algorithm performance with some classical CA-CFAR, SO-CFAR and CA-CFAR followed by a binary pulse-to-pulse integration, in noise, log-normal clutter. The number of guard and reference range cells for CFAR based detectors was 50. The binary integration strategy is of “3-out-of-4.” Real ferry and a boat target have been used

Fig. 9. Proposed, CA-CFAR, SO-CFAR and CA-CFAR detection performance along with binary integrator algorithms for Gaussian noise with “Volcán de Timanfaya” ferry. Pfa =0.001

aid of the proposed algorithm. The automatic alignment method consists of selecting the first target peak detected, and to use this point as the beginning of the profile. The profiles are captured in different vessel positions and days when ferries go out from the harbour. From this figure it is possible to see differences between the two twin ferry profiles and to identify the vessel using only the target profile.

Fig. 10. Proposed and binary integration algorithms detection performance for log-normal clutter distribution with small boat. Pfa =0.001. Fig. 13. Automatic profile alignment from twin ferries "Volcán de Tejeda" (left) and "Volcán de Tauce" (right).

V.

CONCLUSION

In this paper the authors propose an efficient detection algorithm for HRR useful for target identification and a highresolution real database study. From this study it is possible to see differences between the noise, clutter and target width in a more accurate way than in the case of low-resolution radar (longer than 0.5 m).

Fig. 11. Proposed and binary integration algorithms detection performance for log-normal clutter distribution with “Volcán de Timanfaya” ferry. Pfa=0.001.

Some advantages of the proposed detection algorithm to mention are that it is not necessary to know the target length and it is independent of clutter or noise distribution. The performance in clutter presence improves to other compared detectors such as CA-CFAR, SO-CFAR and CA-CFAR with HRR signals. Also the capability of target identification using the proposed detector as profile alignment is presented. New abilities of the proposed mathematical frame for HRR signals are being researched such as target radial speed estimation. REFERENCES [1]

[2] Fig. 12. Ferry goes out from the harbour.

With the aid of this detector it is also possible to detect all the reflectors of a single target in different sea states and distances from the radar. For this study, a real database containing the 400 high-resolution vessel profiles was used and automatically selected with the proposed algorithm. Fig. 12 shows how the ferry goes out from the harbour and different vessel positions. Fig. 13 shows the alignment from twin ferries "Volcán de Tejeda" (left) and "Volcán de Tauce" (right) and it is possible to identify the vessel due to one reflector present in the right figure. The automatic alignment is obtained with the

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