Vol.29 No.3/4 JOURNAL OF ELECTRONICS (CHINA) July 2012

SIMULTANEOUS MULTIPLE TARGET RECOGNITION USING POLARIZATION AGILE WAVES1

Chen Xinwei Zhao Jianzhong Wu Wen (Ministerial Key Laboratory of JGMT, Nanjing University of Science and Technology, Nanjing 210094, China)

Abstract A novel matching method for simultaneous multi-target recognition is proposed by jointly considering target’s prior scattering knowledge and the polarization parameters of radar echoes. The matching coefficients are calculated for the judgment. MATLAB simula-tions show that several targets can be accurately recognized simultaneously, and a high recognition probability can be achieved in Monte Carlo simulations. The total execution time can be remarkably reduced in the Field Programmable Gate Array (FPGA) implementation of the matching procedure.

Key words Matching method; Multiple target recognition; Polarization agile waves; Field Programmable Gate Arrays (FPGA)

CLC index TN95

DOI 10.1007/s11767-012-0813-z

I. Introduction Radar target recognition from a returned signal

has long been one of radar design goals[1,2]. The scattered signals from a target can be affected by the polarization of the radar signal and character-istics of target. It is possible that the differences in the scattered waves from targets viewed with dif-ferent polarizations may be used as a means for distinguishing one target from another in some simple cases[3]. Recent advances in radar design and researches on electromagnetic wave polarimetric theory have motivated intense interest in target recognition in polarization agile radar[4–6]. However, reported methods for target recognition in these papers have been restricted to the case of limited number (often less than two) of different targets because of the complex procedure of recognition process. As a result, there has been a growing in-terest for multiple target recognition in the past few years[7,8]. Especially in Ref. [8], the stochastic optimization algorithm based on a Genetic Algo-rithm (GA) was applied to searching globally for the optimum polarization state for multiple target discrimination. But dozens of iterations were re- 1 Manuscript received date: December 6, 2011; revised date:

April 9, 2012. Communication author: Chen Xinwei, born in 1983, male, Ph.D. student. No. 200, Xiaolingwei Street, Xuanwu District, Nanjing 210094, China. Email: cxwnjust2002@hotmail.com.

quired for the convergence performance of the GA, and large amounts of time were needed during the process. As a result, it is difficult to implement this kind of algorithm for portable devices.

It was suggested from Ref. [8] that two kinds of basic recognition information were considered in target recognition process. They are: (1) the prior scattering knowledge of the interesting targets; (2) the polarization parameters of radar echoes. In Refs. [9,10], the prior scattering knowledge from a mount of different targets was studied in different circumstances, and sufficient polarization knowl-edge of targets was offered for target recognition.

In this paper, we confine our interest to sim-plifying the recognition procedure and achieving simultaneous multiple target recognition by jointly considering the two kinds of basic recognition in-formation mentioned above. The matching level of the polarization parameters of echoes matched with the prior knowledge, defined as matching coeffi-cient, is determined by directly comparing the corresponding parts (amplitude and phase) of the two kinds basic recognition information. Then multiple matching results can be easily acquired simultaneously. The recognition probability is up to 96.3% in Monte Carlo simulations. In the Field Programmable Gate Arrays (FPGA) implemen-tation, the total execution time of the matching procedure is less than 0.1 ms, and it is much shorter than the execution time of GA.

238 JOURNAL OF ELECTRONICS (CHINA), Vol.29 No.3/4, July 2012

The rest of the paper is organized as follows. Matching method is discussed in detail in Section II. Simulation results and analysis are provided in Section III. Finally, conclusion is presented in Section IV.

II. Matching Method In EM polarization theory, the Jones vector is

regarded as a matrix representation of the polari-zation state of the electric field. In radar system, Jones vector E is often represented by the ampli-tudes of the signals in horizontal and vertical channels (aH and aV) and the phase difference ϕ between them, and it can be written as

H

Vj

a

a e ϕ

⎡ ⎤⎢ ⎥= ⎢ ⎥⎢ ⎥⎣ ⎦

E (1)

There is a linear transform between the scat-tered wave sE and the incident wave i,E which is related to the shape, structure and size of the target. The transform can be written as

s i=E SE (2)

where S denotes the Polarization Scattering Matrix (PSM) of the target.

During one recognition process, the system transmits waves in different polarization states. The received Jones vectors, regarded as polariza-tion parameters of radar echoes, can be represented by three vectors as

rec rec rec recH H1 H2 H

rec rec rec recV V1 V2 V

rec rec rec rec1 2

n

n

n

a a a

a a a

ϕ ϕ ϕ

⎧ ⎡ ⎤⎪ =⎪ ⎢ ⎥⎪ ⎣ ⎦⎪⎪⎪ ⎡ ⎤=⎨ ⎢ ⎥⎣ ⎦⎪⎪⎪ ⎡ ⎤⎪ =⎪ ⎢ ⎥⎪ ⎣ ⎦⎩

A

A

D

(3)

In Eq. (3), the superscript rec indicates that the parameters are measured from received signals. The elements in AH and AV denote the normalized amplitudes of signals in horizontal and vertical channel. D is the vector of phase differences, which can be calculated by using the method proposed in Ref. [11]. And n denotes the number of pulses transmitted.

If the number of different targets is r, the vec-tors in Eq. (3) are expanded to three r×n matrixes as

rec recH11 H1

recH

rec recH 1 H

rec recV11 V1

recV

rec recV 1 V

rec rec11 1

rec

rec rec1

n

r rnr n

n

r rnr n

n

r rnr n

a a

a a

a a

a a

ϕ ϕ

ϕ ϕ

×

×

×

⎧ ⎡ ⎤⎪⎪ ⎢ ⎥⎪⎪ ⎢ ⎥⎪ = ⎢ ⎥⎪⎪ ⎢ ⎥⎪⎪ ⎢ ⎥⎪ ⎢ ⎥⎪ ⎣ ⎦⎪⎪⎪ ⎡ ⎤⎪⎪ ⎢ ⎥⎪ ⎢ ⎥⎪⎪ = ⎢ ⎥⎨⎪ ⎢ ⎥⎪ ⎢ ⎥⎪⎪ ⎢ ⎥⎣ ⎦⎪⎪⎪⎪ ⎡ ⎤⎪ ⎢ ⎥⎪⎪ ⎢ ⎥⎪ = ⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎣ ⎦⎩

A

A

D⎪⎪⎪⎪⎪⎪⎪

(4)

The row vector of Eq. (4) denotes that the polarization parameters are from the same target caused by incident waves with different polariza-tion states, and the column vector denotes that the polarization parameters are from different targets caused by incident waves with the same polariza-tion state.

The prior knowledge can also be expressed, like Eq. (4), as

pri priH11 H1n

priH

pri priH 1 H

pri priV11 V1

priV

pri priV 1 V

pri pri11 1

pri

pri pri1

p pnp n

n

p pnp n

n

p pnp n

a a

a a

a a

a a

ϕ ϕ

ϕ ϕ

×

×

×

⎧⎪ ⎡ ⎤⎪ ⎢ ⎥⎪⎪ ⎢ ⎥⎪⎪ ⎢ ⎥=⎪ ⎢ ⎥⎪⎪ ⎢ ⎥⎪⎪ ⎢ ⎥⎪ ⎣ ⎦⎪⎪⎪ ⎡ ⎤⎪⎪ ⎢ ⎥⎪ ⎢ ⎥⎪⎪ ⎢ ⎥=⎨ ⎢ ⎥⎪⎪ ⎢ ⎥⎪⎪ ⎢ ⎥⎪ ⎣ ⎦⎪⎪⎪ ⎡ ⎤⎪⎪ ⎢ ⎥⎪ ⎢ ⎥⎢ ⎥=⎢ ⎥⎢ ⎥⎢ ⎥⎣ ⎦⎩

A

A

D⎪⎪⎪⎪⎪⎪⎪⎪⎪

(5)

The superscript pri indicates that the values have been measured in advance, and they are stored in the system before the recognition. The kind of prior knowledge is indicated by p.

It is assumed that the targets are considered as point targets and the radial distance between each target is larger than the range resolution of radar system. The polarization parameters of radar echoes are represented according to Eq. (4) in the signal processing module. Assuming that there are

CHEN et al. Simultaneous Multiple Target Recognition Using Polarization Agile Waves 239

r targets in the detection range of radar, the matching coefficient mkl, indicating the level of the k-th ( )k r≤ target’s polarization parameters matched with the 1 (1 )t p− ≤ kind of prior knowledge, can be calculated as

( ) ( )

( )

rec pri rec priH H V V

1 1

rec pri

1

n n

kl ki li ki lii i

n

ki lii

m a a a a

ϕ ϕ

= =

=

= − + −

+ −

∑ ∑

∑ (6)

In simultaneous multiple target recognition, the scattered waves of all targets are received by the system during one recognition process, and then the polarization parameters are matched with all the prior knowledge. As a result, an r×p matrix M is made up by all matching coefficients as

11 1

1

p

r rpr p

m m

m m×

⎡ ⎤⎢ ⎥⎢ ⎥= ⎢ ⎥⎢ ⎥⎢ ⎥⎣ ⎦

M (7)

In this paper, we define the criterion coefficient cu ( ),u r≤ which equals to the element with the minimum absolute value in the u-th row of M, denoted as muv. It can be inferred from Eq. (6) that the u-th target to be recognized is best matched with the v-th kind of prior knowledge. The criterion coefficients of all targets can be illustrated in a vector C as

( )

( )

1 1 11

1

min

,

min

v jj p

r rvrjj p

c m m

v p

c m m

≤ ≤

≤ ≤

⎡ ⎤⎡ ⎤ ⎡ ⎤ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥= = = ≤⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎣ ⎦ ⎣ ⎦ ⎢ ⎥⎣ ⎦

C (8)

There is a threshold t for each criterion coeffi-cient. If the coefficient is less than the threshold, the detected target is recognized. Otherwise, it is regarded as an unknown target. The judgment process of the u-th target can be expressed as

uc t≤ → recognized as the v-th kind of prior in-

formation (9)

uc t> → unknown target (10)

III. Simulation and Results The simulations in this paper are carried out in

MATLAB environment based on the radar system prototype shown in Fig. 1. It is supposed that the stored prior knowledge contains four kinds of target information (p=4), which are shown in Tab. 1. The selected polarization states (n=4) of transmitting signals are shown in Tab. 2. Because the prior knowledge of targets is concerned with the envi-ronment, the profile and azimuth of targets and other related factors, it is important to properly choose the prior knowledge according to the discu-

Fig. 1 Polarization agile radar system prototype

ssions in Refs. [9,10] in real applications. The first, second, and fourth targets in Tab. 1

are chosen at random for recognition in the simu-lation (r=3). The SNR of the receiver is supposed to be 20 dB. Tab. 3 shows the matrixes of prior knowledge and the polarization parameters of radar echoes in the simulation.

Some elements in D are denoted as “–” due to the zero value of amplitude in horizontal or vertical channel. Because the advance or lag of the phase difference in two channels cannot be judged by the calculation method in Ref. [11], the remaining elements in D are supposed to be non-negative.

Then matrix M is calculated by Eq. (6) as

240 JOURNAL OF ELECTRONICS (CHINA), Vol.29 No.3/4, July 2012

0.56 5.83 4.07 1.44

4.97 0.30 1.46 4.09

0.56 4.71 2.95 0.32

⎡ ⎤⎢ ⎥⎢ ⎥

= − − −⎢ ⎥⎢ ⎥⎢ ⎥−⎢ ⎥⎣ ⎦

M (11)

Tab. 1 Four kinds of stored prior knowledge in the system*

Target PSM

Sphere 1 0

0 1

⎡ ⎤⎢ ⎥⎢ ⎥⎢ ⎥⎣ ⎦

Long level line 1 0

0 0

⎡ ⎤⎢ ⎥⎢ ⎥⎢ ⎥⎣ ⎦

Random 1 0.82 0.25

0.33 0.26

⎡ ⎤⎢ ⎥⎢ ⎥⎢ ⎥⎣ ⎦

Random 2 6

0.19 0.52

0.75 0.34j

eπ

⎡ ⎤⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎣ ⎦

* Targets are supposed to be in a certain profile.

Tab. 2 Chosen polarization states of transmitting waves

Polarization state Jones vector

Horizontal 1

0

⎡ ⎤⎢ ⎥⎢ ⎥⎢ ⎥⎣ ⎦

45° linear 1112

⎡ ⎤⎢ ⎥⎢ ⎥⎢ ⎥⎣ ⎦

Left-hand circular 11

2 j

⎡ ⎤⎢ ⎥⎢ ⎥⎢ ⎥⎣ ⎦

Right-hand circular 11

2 j

⎡ ⎤⎢ ⎥⎢ ⎥−⎢ ⎥⎣ ⎦

The criterion vector C and the positions of co-

efficient can be written as

Column 10.56

0.30 Column 2

0.32 Column 4

⎡ ⎤→⎢ ⎥⎢ ⎥

= →⎢ ⎥⎢ ⎥⎢ ⎥→⎢ ⎥⎣ ⎦

C (12)

The threshold is chosen as 1,t ≤ comprehen-sively considering the circumstance of surroundings and the system performance. The recognition re-sults obtained by Eqs. (9) and (10) are as follows.

Target 1 is described by the first kind of prior knowledge.

Target 2 is described by the second kind of prior knowledge.

Target 3 is described by the fourth kind of prior knowledge.

Tab. 3 The matrixes of prior knowledge and polarization pa-rameters of radar echoes

Matrix Prior knowledge Polarization parameters

of radar echoes

AH

1 0.71 0.71 0.71

1 0.71 0.71 0.71

0.82 0.76 0.61 0.61

0.19 0.71 0.55 0.55

⎡ ⎤⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎣ ⎦

1.12 0.78 0.72 0.81

1.08 0.80 0.65 0.80

0.23 0.74 0.53 0.58

⎡ ⎤⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎣ ⎦

AV

0 0.71 0.71 0.71

0 0 0 0

0.33 0.42 0.30 0.30

0.75 0.78 0.75 0.47

⎡ ⎤⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎣ ⎦

0.03 0.75 0.75 0.73

0.02 0.03 0.01 0.04

0.82 0.79 0.80 0.52

⎡ ⎤⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎣ ⎦

D

0 1.57 1.57

0 0 0.37 0.37

0.53 0.47 0.31 1.46

⎡ − ⎤⎢ ⎥⎢ ⎥− − − −⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎣ ⎦

0.12 1.55 1.60

0.59 0.44 0.29 1.51

⎡ ⎤−⎢ ⎥⎢ ⎥− − − −⎢ ⎥

⎢ ⎥⎢ ⎥⎢ ⎥⎣ ⎦

It is demonstrated that the targets can be

recognized correctly from the results above. Monte Carlo simulations are performed for 100

times as the process above, on the condition that SNR is constant. Recognition probability is up to 96.3% based on the simulation results.

Then the matching procedure is implemented on an Altera EP1C6 FPGA, and the modules are designed in parallel architectures. When TADD and TCOMP denote the execution time of the major modules (adder and comparator) of the design, the total time necessary to obtain the final recognition results will be TTOTAL

TOTAL ADD COMPT nT pT= + (13)

If the values of n and p are finite (e.g. less than 100), it can be estimated that TTOTAL is absolutely less than 0.1 ms, while the values of TADD and TCOMP are referred to Ref. [12]. However, it will take several milliseconds to obtain recognition results, when GA is implemented on an FPGA[13], although all targets are almost correctly recognized in the experiment in Ref. [8] by using GA.

IV. Conclusion In this paper, a new matching method is con-

sidered to find out the matching coefficients of two kinds of basic recognition information in polariza-

CHEN et al. Simultaneous Multiple Target Recognition Using Polarization Agile Waves 241

tion agile radars. Simplified operations (addition and comparison) are included in the matching procedure and can be easily implemented on a chip- based system. The total execution time is short so that the method is appropriate for real-time ap-plications. The results of MATLAB simulations show that the matching method can be widely used in simultaneous multiple target recognition in dif-ferent portable types of polarization agile radars. We can achieve a more precise recognition by this method, as the prior knowledge database of targets becomes larger.

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