A neural network approach to target classification for active safety system using microwave radar

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Abstract

As a sensor in the active safety system of vehicles, the microwave radar (MWR) would be a good choice for the localization of the nearby targets but could be a bad choice for their classification or identification. In this paper, a target classification system using a 24 GHz microwave radar sensor is proposed for the active safety system. The basic idea of this paper is that the pedestrians and the vehicles have different reflection characteristics for a microwave. A multilayer perceptron (MLP) neural network is employed to classify the targets and the probabilistic fusion is conduct over time to improve the classification accuracy. Some experiments are performed to show the validity of the proposed system.

Introduction

Safety has been a hot issue in recent vehicular technology and a tremendous research has been conducted towards the direction. The researches concerning the safety of the passengers and drivers in the vehicles have produced two paradigms: passive safety system and active safety system. The passive safety system purposes to minimize the damage after car accident and an air bag and safety belt belong to this class (Chan, 2007, Watanabe et al., 1994). The passive system does not aim at reducing the possibility of the car accidents. On the contrary, active safety system purposes to prevent the car accidents before they occur and it is now receiving much attention within vehicular community. Mainly, the active safety system recognizes the surrounding environment around its own car and alerts the car driver about the nearby possible dangers. The road sign recognition system (Nguwi and Kouzani, 2006, Yoon et al., 2008), and the blind spot warning system (Krips et al., 2004, US Patent 6859148, 2005), the adaptive cruise control (ACC) system (Bifulco et al., 2008, Ioannou and Stefanovic, 2005, Wang et al., 2007) and pedestrian protection systems (PPS) (Gandhi & Trivedi, 2007) certainly belong to the active safety system.

In the active safety system, the key technology is the understanding of the surrounding objects, that is, detection, tracking and identification of the nearby objects. For the purpose, several sensors are used and CCD cameras and range finders are the most common ones. The CCD camera returns rich information about the nearby target objects and provides relatively easy method for target recognition. However, it is difficult to measure the range to the nearby targets from the car. On the contrary, range finders such as a laser scanner or microwave radar easily measure the location of nearby objects and are robust to the variation of the weather or time. But the range finders have difficulty in recognizing the target objects (Fuerstenberg and Dietmayer, 2004, Fuerstenberg and Willhoeft, 2001, Maclachlan and Mertz, 2006).

In this paper, we develop a new microwave radar-based target classification system for an active safety system. We do not use a CCD camera but use only a 24 GHz microwave radar to classify the nearby objects. Thus, with a single radar, we can fulfill both target tracking and target recognition simultaneously at no extra cost. The basic idea of the target recognition by a microwave radar is that the pedestrians and the vehicles have different reflection characteristics for a microwave. Based on the idea, we build a classifier using a multilayer perceptron neural network (MLP). Further, we present a probabilistic fusion method to make a classification decision based on not only the current measurement but also all the past measurements.

The remaining of this paper is organized as follows: in Section 2, the microwave radar sensor and the experimental setup are explained. In Section 3, the problem is formulated and a neural network classifier is designed for target classification. In Section 4, the neural network classifier results are fused over time to improve the classification result. Finally, some conclusions are drawn in Section 5.

Section snippets

24 GHz microwave radar sensor

In this paper, a 24 GHz microwave radar (MWR) named MASRAU0025 is employed to sense and classify targets around the vehicle. The radar is manufactured by M/A-COM and is composed of a 24 GHz radar sensor and microprocessor unit (Manual of MA-COM MASRAU0025, 2005). Fig. 1 shows the configuration of our experimental setup for target classification. As in Fig. 1, the 24 GHz microwave sensor MASRAU0025 is mounted on the car and is connected to a computer through CAN (Control Area Network). In the

Multilayer perceptron neural network classifier

Neural networks imitate the human brains and acquire knowledge about the hidden relationships between input and output directly from samples (Duda et al., 2001, Hagan et al., 1995). Fig. 3 shows a three layer MLP with input, hidden, and output layers.

Suppose we are given data set X=x1,x2,…,xN and each data xn=x1n,x2n,…,xmnT∈Rm belongs to one of C classes ωl(l=1,2,…C), where C is the number of classes, ωks are disjointed and Ω=ω1∪ω2∪…∪ωC. Then the output of neural networks shown in Fig. 3 is

Probabilistic target classification

The target classification based on MLP sometimes makes a wrong decision due to outliers produced by the microwave radar. To solve this problem, we utilize not only the current measurement but also all the previous measurements in a probabilistic framework to make a decision about the class of the target. Here, the measurement means the features used in the MLP classification: distance, bearing and received signal power. More specifically, in Section 3, we used the MLPglNN(xt)≈p(ωl|xt)in making

Experimental results

In this section, we conduct some experiments to show the effectiveness of the proposed method. In the probabilistic classification, we set both bel0(ω=V) and bel0(ω=P) to 0.5 since we have no prior information about the target. We use the same MLP trained from 1853 vehicle samples and 2202 pedestrian samples in the previous section. Fig. 7 shows our experimental setup in which both the vehicle and the pedestrian move at almost the same speed.

MLP makes a soft decision and it is fused over time

Conclusion

In this paper, we have developed a target classification for active safety system. We implemented it using only using a microwave radar without the help of any CCD camera. Since we used only a 24 GHz radar, we can fulfill both target tracking and recognition simultaneously at no extra cost. Our system was built and its validity was demonstrated through a real world experimentation. The contribution of this paper is threefold:

  • (1)

    Target classification system for an active safety system is implemented

Acknowledgements

This work is supported by Mando Co., Active Protection Pedestrian System Project.

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