JP6993136B2 - Radar device and target detection method - Google Patents

Description

The disclosed embodiments relate to radar devices and target detection methods.

Conventionally, a radar device that performs filtering processing to track a detected target is known (see, for example, Patent Document 1).

Japanese Unexamined Patent Publication No. 2015-028440

However, the radar device does not consider tracking a vehicle, a bicycle, or a pedestrian having a different type of target, for example, a behavior pattern.

One aspect of the embodiment is made in view of the above, and an object thereof is to provide a radar device and a target detection method capable of tracking different types of targets.

The radar device according to one embodiment includes an identification unit, a setting unit, and a generation unit. The identification unit identifies the type of target. The setting unit sets a filter in which at least one of the filter type and the parameter value of the filter is different so that the filtering process for generating the target data related to the target by the time series filtering differs depending on the type of the target. The generation unit generates target data related to the target using the set filter.

According to one aspect of the embodiment, different types of targets can be tracked.

FIG. 1 is a diagram illustrating a target detection method according to an embodiment. FIG. 2 is a block diagram showing a configuration of a radar device according to an embodiment. FIG. 3 is an explanatory diagram of processing from the pre-stage processing of the signal processing unit to the peak extraction processing in the signal processing unit. FIG. 4A is a processing explanatory diagram of the angle estimation process. FIG. 4B is a process explanatory diagram (No. 1) of the pairing process. FIG. 4C is a process explanatory diagram (No. 2) of the pairing process. FIG. 5 is a diagram showing power values in pedestrians and other vehicles. FIG. 6 is a diagram showing speed spread in pedestrians and other vehicles. FIG. 7 is a diagram showing the relationship between the distance to the target and the maximum intensity and the intensity spread. FIG. 8 is a flowchart illustrating a target data generation process according to the embodiment.

Hereinafter, the radar device and the target detection method disclosed in the present application will be described with reference to the attached drawings. The present invention is not limited to the embodiments shown below.

Hereinafter, the case where the radar device 1 is of the FM-CW (Frequency Modulated Continuous Wave) method will be described as an example, but the radar device 1 is another method such as the FCM (Fast-Chirp Modulation) method. May be good.

First, the target detection method by the radar device 1 will be described with reference to FIG. FIG. 1 is a diagram illustrating a target detection method according to an embodiment.

The radar device 1 is mounted in the front grill of the vehicle, for example, and detects a target (for example, another vehicle (vehicle), a bicycle, a pedestrian (person), etc.) existing in the traveling direction of the own vehicle MC. do. The radar device 1 may be mounted on other places such as a windshield, a rear grill, and left and right side portions (for example, left and right door mirrors).

The radar device 1 detects the observed value based on the transmitted wave transmitted toward the target and the reflected wave of the transmitted wave by the target (S1).

Next, the radar device 1 identifies the type of the target based on the observed value (S2). The radar device 1 identifies the type of the target by using the identification process by supervised machine learning. Machine learning will be described later. The radar device 1 identifies the type of the target by the identification process by machine learning. Specifically, the radar device 1 identifies targets of "another vehicle", "bicycle", and "pedestrian".

Next, the radar device 1 sets a filter corresponding to the type of the identified target (S3). The radar device 1 sets a filter to be used in the filter processing for each type of target. Specifically, the radar device 1 sets a filter to be used depending on whether the target is "another vehicle", "bicycle", or "pedestrian". That is, the radar device 1 sets a filter to be used in the filter processing for each target having a different behavior pattern.

The filter used is, for example, a linear Kalman filter which is a linear filter, an extended Kalman filter which is a non-linear filter, and a particle filter which is a non-linear filter.

The Kalman filter calculates the optimum Kalman gain that minimizes both errors based on the latest observation value including the error and the previous state data including the error, and uses the optimum Kalman gain to calculate the latest state data from the previous state data. It predicts state data.

The linear Kalman filter according to the embodiment moves the previous target data according to a motion model (constant velocity linear model), assuming that both the obtained latest observation value and the previous target data contain an error. In this case, the optimum Kalman gain that minimizes the error is calculated. Then, the linear Kalman filter uses the motion model and the optimum Kalman gain to generate the current target data from the current predicted value and the latest observed value based on the previous target data. The linear Kalman filter is suitable for tracking a target having a smaller processing load than, for example, a particle filter, and a movement fluctuation per short time, specifically, a movement fluctuation in a traveling direction is small. For example, in the case of another vehicle, it is unlikely that the traveling direction will switch from the straight direction to the backward direction in a short time (for example, for 1 second), but in the case of a pedestrian, the traveling direction may change in a short time. Highly sex. Therefore, in the case of other vehicles as described later, a linear Kalman filter is used.

The extended Kalman filter is a filter that applies the above-mentioned linear Kalman filter method by performing linear approximation to a nonlinear model. The extended Kalman filter is more suitable for tracking targets with large movement fluctuations per short time than the linear Kalman filter. For example, in the case of a bicycle, the possibility that the traveling direction changes in a short time is higher than that of other vehicles and lower than that of a pedestrian. Therefore, in the case of a bicycle as described later, an extended Kalman filter is used.

The particle filter estimates and generates target data by plotting a predetermined number of particle data and observed values in a predetermined state space and analyzing the positional relationship in the state space.

Specifically, in the particle filter, target data is generated from the observed values by performing the processes of "prediction", "allocation", "weighting", "resampling", and "data generation".

"Prediction" is a process of predicting the distribution state of particle data used in the latest cycle in the state space. Specifically, "prediction" is a prediction process for predicting the distribution state in the current particle data, which is the latest cycle, from the distribution state in the previous particle data, which is the previous cycle.

"Assignment" is a process of allocating the observed value of this time, which is the latest cycle, to the predicted particle data of this time. In "assignment", for example, observation values existing in a predetermined allocation range from the previous target data are assigned to the current particle data.

"Weighting" is a process of weighting each of the current particle data based on the assigned current observation value.

"Resampling" is a process of rearranging (resampling) each of the current particle data based on the weight of each of the current particle data by "weighting".

"Data generation" is a process of generating the target data of this time from the observed value of this time based on the resampled particle data of this time.

The particle filter requires a large number of particle data and has a larger processing load than the linear Kalman filter and the extended Kalman filter, but is suitable for tracking a target having a large movement fluctuation per short time.

When the type of the target is "another vehicle", the radar device 1 determines that the movement fluctuation is small as described above, and sets the filter used in the filter processing to the linear Kalman filter.

Further, when the type of the target is "bicycle", the radar device 1 determines that the movement fluctuation is larger than that of "another vehicle" as described above, and sets the filter used in the filter processing to the extended Kalman filter. ..

Further, when the target type is "pedestrian", the radar device 1 determines that the movement fluctuation is larger than that of "other vehicle" and "bicycle", and sets the filter used in the filter processing to the particle filter. do.

In this way, the radar device 1 uses the radar device 1 for a target that is likely to have a large difference (movement fluctuation) between the movement of the target up to the present (for example, the traveling direction and speed) and the next movement (movement fluctuation). A particle filter with high followability to changes in the operation of is adopted. Further, the radar device 1 has a linear Kalman filter or an extension that reduces the processing load, although the tracking ability to the change in the movement of the target is lower than that of the particle filter for the target whose movement fluctuation of the target is unlikely to be significantly different. Apply the Kalman filter.

The large difference in movement fluctuation means, for example, a case where the speed difference differs by 20 km / h or more in a short time (for example, 1 second), specifically, a case where a running person suddenly stops. .. In addition, a large difference in movement fluctuation means that the direction of travel is opposite. Specifically, a pedestrian turns from one sidewalk side to the pedestrian crossing provided on the roadway toward the other sidewalk side. This is the case when the direction of travel is changed in the opposite direction and the vehicle returns to one sidewalk while moving. Further, the low movement fluctuation means, for example, acceleration / deceleration of a vehicle whose speed difference is less than 20 km / h in a short time.

Next, the radar device 1 performs filter processing for each target using each filter set according to the type of the target, and generates target data corresponding to the observed value of the target (S4). .. That is, the radar device 1 generates target data for each target by using a filter set for each target of a different type. In this way, the radar device 1 tracks different types of targets using different filters.

The radar device 1 can improve the traceability of targets of different types by generating target data using a filter suitable for the type of target.

Next, the configuration of the radar device 1 according to the embodiment will be described with reference to FIG. FIG. 2 is a block diagram showing the configuration of the radar device 1 according to the embodiment.

The radar device 1 includes a transmitting unit 10, a receiving unit 20, and a processing unit 30. The radar device 1 is connected to a vehicle control device 2 that controls the behavior of the own vehicle MC.

The vehicle control device 2 controls a vehicle such as a PCS (Pre-crash Safety System) or an AEB (Advanced Emergency Braking System) based on the detection result of a target by the radar device 1.

The transmission unit 10 includes a signal generation unit 11, an oscillator 12, and a transmission antenna 13. The signal generation unit 11 generates a modulated signal for transmitting a millimeter wave frequency-modulated by a triangular wave under the control of a transmission / reception control unit 31 described later. The oscillator 12 generates a transmission signal based on the modulation signal generated by the signal generation unit 11 and outputs the transmission signal to the transmission antenna 13. As shown in FIG. 2, the transmission signal generated by the oscillator 12 is also distributed to the mixer 22 described later.

The transmitting antenna 13 converts the transmitted signal from the oscillator 12 into a transmitted wave, and outputs the transmitted wave to the outside of the own vehicle MC. The transmitted wave output by the transmitting antenna 13 is a continuous wave frequency-modulated with a triangular wave. The transmitted wave transmitted from the transmitting antenna 13 to the outside of the own vehicle MC, for example, to the front, is reflected by a target such as the preceding vehicle LC and becomes a reflected wave.

The receiving unit 20 includes a plurality of receiving antennas 21 forming an array antenna, a plurality of mixers 22, and a plurality of A / D conversion units 23. The mixer 22 and the A / D conversion unit 23 are provided for each receiving antenna 21.

Each receiving antenna 21 receives the reflected wave from the target as a received wave, converts the received wave into a received signal, and outputs the received wave to the mixer 22. Although the number of receiving antennas 21 shown in FIG. 2 is 4, it may be 3 or less or 5 or more.

The received signal output from the receiving antenna 21 is amplified by an amplifier (for example, a low noise amplifier) (not shown) and then input to the mixer 22. The mixer 22 mixes a part of the distributed transmission signal and the reception signal input from the reception antenna 21 to remove unnecessary signal components to generate a beat signal, and outputs the beat signal to the A / D conversion unit 23. ..

The beat signal is a difference wave between the transmitted wave and the reflected wave, and is the frequency of the transmitted signal (hereinafter referred to as “transmission frequency”) and the frequency of the received signal (hereinafter referred to as “reception frequency”). It has a different beat frequency. The beat signal generated by the mixer 22 is converted into a digital signal by the A / D conversion unit 23, and then output to the processing unit 30.

The processing unit 30 includes a transmission / reception control unit 31, a signal processing unit 32, and a storage unit 33.

The transmission / reception control unit 31 controls the transmission unit 10 including the signal generation unit 11 and the reception unit 20. The signal processing unit 32 periodically executes a series of signal processing.

The storage unit 33 stores the history data 33a. The history data 33a is information including a history of target data in a series of signal processing executed by the signal processing unit 32.

The processing unit 30 is, for example, a microcomputer including a CPU (Central Processing Unit), a ROM (Read Only Memory) and a RAM (Random Access Memory) corresponding to the storage unit 33, a register, and other input / output ports, and is a radar. Controls the entire device 1.

By reading and executing the program stored in the ROM, the CPU of the microcomputer functions as the transmission / reception control unit 31 and the signal processing unit 32. The transmission / reception control unit 31 and the signal processing unit 32 can all be configured by hardware such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

The signal processing unit 32 includes a frequency analysis unit 32a, a peak extraction unit 32b, an observation value generation unit 32c, an identification unit 32d, a filter setting unit 32e, and a filter processing unit 32f.

The frequency analysis unit 32a performs a fast Fourier transform (FFT) process (hereinafter referred to as “FFT process”) on the beat signal input from each A / D transform unit 23, and peaks the result. Output to the extraction unit 32b. The result of the FFT processing is the frequency spectrum of the beat signal, and is the power value (signal level) for each frequency of the beat signal (for each frequency bin set at a frequency interval corresponding to the frequency resolution).

The peak extraction unit 32b extracts the peak frequency that becomes the peak in the result of the FFT processing by the frequency analysis unit 32a, and outputs the extraction result to the observation value generation unit 32c. The peak extraction unit 32b extracts peak frequencies for each of the "UP section" and "DN section" of the beat signal described later.

The observation value generation unit 32c executes an angle estimation process for calculating the arrival angle of the reflected wave corresponding to each of the peak frequencies extracted by the peak extraction unit 32b and the power value thereof. Since the arrival angle is the angle at which the target is estimated to exist at the time of executing the angle estimation process, it may be referred to as "estimated angle" below.

Further, the observation value generation unit 32c executes a pairing process for determining the correct combination of the peak frequencies of the “UP section” and the “DN section” based on the calculation result of the calculated estimated angle and the power value.

Further, the observation value generation unit 32c calculates the distance and the relative speed of the observation value corresponding to each target with respect to the own vehicle MC from the determined combination result.

In order to make the explanation easy to understand, FIGS. 3 to 4C show the flow of processing from the previous stage processing of the signal processing unit 32 to this point in the signal processing unit 32. FIG. 3 is an explanatory diagram of processing from the pre-stage processing of the signal processing unit 32 to the peak extraction processing in the signal processing unit 32.

Further, FIG. 4A is a processing explanatory diagram of the angle estimation process. Further, FIGS. 4B and 4C are processing explanatory views (No. 1) and (No. 2) of the pairing process. Note that FIG. 3 is divided into three regions by two thick downward white arrows. In the following, each area will be described as an upper row, a middle row, and a lower row in order.

As shown in the upper part of FIG. 3, the transmission signal fs (t) is transmitted from the transmission antenna 13 as a transmission wave, then reflected by the target and arrives as a reflected wave, and the reception signal fr (t) is transmitted by the reception antenna 21. ) Is received.

At this time, as shown in the upper part of FIG. 3, the received signal fr (t) is delayed by a time difference T with respect to the transmitted signal fs (t) according to the distance between the own vehicle MC and the target. Due to this time difference T and the Doppler effect based on the relative speed of the own vehicle MC and the target, the beat signal has the frequency up in the "UP section" where the frequency rises and the frequency fdn in the "DN section" where the frequency falls. Is obtained as a signal in which is repeated (see the middle stage of FIG. 3).

The lower part of FIG. 3 schematically shows the result of FFT processing of the beat signal in the frequency analysis unit 32a for each of the "UP section" side and the "DN section" side.

As shown in the lower part of FIG. 3, after the FFT processing, waveforms in the respective frequency domains on the “UP section” side and the “DN section” side are obtained. The peak extraction unit 32b extracts the peak frequency that becomes the peak in the waveform.

For example, in the case of the example shown in the lower part of FIG. 3, the peak extraction threshold value is used, and on the “UP section” side, the peaks Pu1 to Pu3 are determined as peaks, and the peak frequencies fu1 to fu3 are extracted, respectively.

Further, on the "DN section" side, the peaks Pd1 to Pd3 are determined as peaks, respectively, and the peak frequencies fd1 to fd3 are extracted, respectively, by the peak extraction threshold value.

Here, the frequency component of each peak frequency extracted by the peak extraction unit 32b may be a mixture of reflected waves from a plurality of targets. Therefore, the observation value generation unit 32c performs an angle estimation process for calculating the direction of each peak frequency, and analyzes the existence of the corresponding target for each peak frequency.

The directional calculation in the observation value generation unit 32c can be performed by using a known arrival direction estimation method such as ESPRIT (Estimation of Signal Parameters via Rotational Invariance Techniques).

FIG. 4A schematically shows the orientation calculation result of the observation value generation unit 32c. The observation value generation unit 32c calculates the estimated angle of each target (each reflection point) corresponding to each of the peaks Pu1 to Pu3 from the peaks Pu1 to Pu3 of the directional calculation result. Further, the magnitude of each peak Pu1 to Pu3 is the power value. As shown in FIG. 4B, the observation value generation unit 32c performs the angle estimation process for each of the “UP section” side and the “DN section” side.

Then, the observed value generation unit 32c performs a pairing process in which the peaks having similar estimated angles and power values are combined in the directional calculation result. Further, from the combination result, the observation value generation unit 32c calculates the distance and the relative velocity of each target (each reflection point) corresponding to the combination of each peak.

The distance can be calculated based on the relationship of "distance ∝ (fup + fdn)". The relative velocity can be calculated based on the relationship of "velocity ∝ (fup-fdn)". As a result, as shown in FIG. 4C, a pairing processing result showing the instantaneous values of the estimated angle, distance and relative speed of each reflection point RP with respect to the own vehicle MC is obtained.

Further, the observation value generation unit 32c clusters from the determined combination result, and detects, for example, observation values satisfying predetermined conditions such as close detection positions and close relative velocities as observation values corresponding to one target. .. That is, the observation value generation unit 32c groups the observation values.

Further, the observation value generation unit 32c outputs the estimated angle, distance and relative velocity of the target to the identification unit 32d as observation values for the latest cycle (latest scan).

Returning to FIG. 2, the identification unit 32d identifies the types of grouped observation values, that is, the types of targets. The identification unit 32d identifies the type of the target by using the identification process by supervised machine learning. The identification unit 32d identifies the type of the target by, for example, an SVM (Support Vector Machine).

In machine learning, teacher data is input by a learning process performed in advance, and a separation line that separates the types of targets is generated. The information on the separation line is stored in the storage unit 33. The identification unit 32d extracts the feature amount of the target, and identifies the target based on the stored separation line and the feature amount of the extracted target.

The features of the target are range spread, maximum intensity, strength spread and velocity spread.

The range spread is the frequency difference (distance) between two values that serve as the peak extraction threshold in the waveform from which each peak frequency is extracted.

As shown in FIG. 5, the range expansion becomes larger when the target is "another vehicle" than when the target is "pedestrian". FIG. 5 is a diagram showing power values in pedestrians and other vehicles. FIG. 5 shows the power value when another vehicle is detected at a position close to the own vehicle MC and a pedestrian is detected at a position farther than the other vehicle. The range expansion increases as the target becomes larger, and increases in the order of "pedestrian", "bicycle", and "other vehicle".

The maximum intensity and intensity spread indicate the reflection intensity at the target. The maximum intensity is the power value at the peak frequency. The intensity spread is the difference between the power value at the peak frequency and the peak extraction threshold.

As shown in FIG. 5, when the target is "another vehicle", the maximum intensity and the intensity spread are larger than when the target is "pedestrian". The maximum strength and strength spread increase in the order of "pedestrian", "bicycle", and "other vehicle".

The speed spread is a variation in the speed of the observed value included in the target, and as shown by the dots in FIG. 6, it becomes large when the target is a "pedestrian" and the target is "another vehicle". In some cases, it becomes smaller as shown by a straight line. FIG. 6 is a diagram showing speed spread in pedestrians and other vehicles. Note that FIG. 6 shows a case where the vehicle speed of another vehicle is constant. When the target is a "pedestrian", the transmitted wave is reflected from places with a lot of movement such as hands and feet, and the speed variation of the observed value becomes large, and the speed spread becomes large. Further, when the target is "another vehicle", the speed variation of the observed value is small, so that the speed spread is small. In this way, the speed spread increases in the order of "other vehicle", "bicycle", and "pedestrian" when there is a lot of movement in the target.

It should be noted that the above-mentioned feature amount is affected by the change in distance, and in particular, the intensity spread and the maximum intensity are greatly affected by the change in distance because the received intensity of the reflected wave is attenuated by the fourth power of the distance.

For example, as shown in FIG. 7, as the distance from the own vehicle MC to the target increases, the maximum intensity and the intensity spread decrease. FIG. 7 is a diagram showing the relationship between the distance to the target and the maximum intensity and the intensity spread. In FIG. 7, the change in maximum intensity is shown by a solid line, and the change in intensity spread is shown by a broken line.

Therefore, for example, it may be divided into a plurality of range groups according to the distance to the target, machine learning may be performed for each range group, and a separation line may be generated. For example, as shown in FIG. 7, the group may be divided into three range groups (A group, B group, and C group), and a separation line may be generated for each group.

The identification unit 32d identifies the type of the target based on the information of the separation line and the extracted feature amount. The identification unit 32d is not limited to the above identification method, and may identify the type of the target by, for example, deep learning. Further, the feature amount is not limited to the above feature amount, and other feature amounts may be used.

The filter setting unit 32e sets a filter corresponding to the type of the target identified by the identification unit 32d. That is, the filter setting unit 32e sets filters having different types of filters so that the filter processing differs depending on the type of the target.

When the type of the target is "another vehicle", the filter setting unit 32e sets the filter to a linear Kalman filter which is a linear filter. The filter may be an αβ filter or an αβγ filter, which is a linear filter.

When the target type is "bicycle", the filter setting unit 32e sets the filter to the extended Kalman filter which is a non-linear filter. The filter may be an unscented Kalman filter which is a non-linear filter.

When the target type is "pedestrian", the filter setting unit 32e sets the filter to a particle filter which is a non-linear filter. The filter may be an unscented particle filter which is a non-linear filter.

The filter processing unit 32f performs filter processing for each target using the set filter, and generates target data for each target. The filter processing unit 32f performs time-series filtering on the observed value for each target using the set filter, and generates target data as the filter value.

The filter processing unit 32f performs filter processing using a linear Kalman filter on the observed value of the target identified as "another vehicle", and obtains the target data for the target identified as "another vehicle". Generate.

Further, the filter processing unit 32f performs filter processing using the extended Kalman filter on the observed value of the target identified as "bicycle", and obtains the target data for the target identified as "bicycle". Generate.

Further, the filter processing unit 32f performs a filter process using a particle filter on the observed value of the target identified as a “pedestrian”, and the target for the target identified as a “pedestrian”. Generate data.

Next, the target data generation process according to the embodiment will be described with reference to the flowchart of FIG. FIG. 8 is a flowchart illustrating a target data generation process according to the embodiment.

The radar device 1 detects an observed value corresponding to the target based on the frequency-modulated transmitted wave and the reflected wave of the transmitted wave by the target (S10).

The radar device 1 groups the observed values (S11) and identifies the grouped observed values, that is, the type of the target (S12).

The radar device 1 sets a filter corresponding to the type of the identified target (S13). Then, the radar device 1 performs filter processing according to the type of the target using the set filter, and generates target data (S14).

Next, the effect of the radar device 1 according to the embodiment will be described.

The radar device 1 identifies the type of the target and sets a filter according to the type of the identified target. Then, the radar device 1 performs a filter process using the set filter to generate target data. This makes it possible to track different types of targets. In addition, it is possible to improve the traceability for targets having different movement changes (behavior patterns).

Further, the radar device 1 can reduce the processing load when tracking the target by setting a filter for each type of the identified target and generating the target data. For example, when tracking the target of "other vehicle" and generating the target data of "other vehicle", the observation value of "pedestrian" near the observation value of "other vehicle" is the target. Since the types are different, they are not considered. That is, when tracking one type of target, information on another type of target can be excluded. Therefore, the processing load for tracking the target can be reduced. In addition, since the information of other types of targets can be excluded, the traceability of the targets can be improved.

When the target is "another vehicle", the radar device 1 sets the filter used in the filter processing to a linear filter, and when the target is "bicycle" or "pedestrian", the filter processing is performed. Set the filter to be used as a non-linear filter. As a result, it is possible to improve the trackability for "bicycles" and "pedestrians" whose movement fluctuations are larger than those of "other vehicles".

When the target is "another vehicle", the radar device 1 sets the filter used in the filtering process to the linear Kalman filter which is a linear filter. As a result, the processing load when tracking the "other vehicle" can be reduced.

When the target is a "bicycle", the radar device 1 sets the filter used in the filtering process to the extended Kalman filter which is a non-linear filter. This makes it possible to improve the trackability of the "bicycle", which has a larger movement fluctuation than the "other vehicle".

When the target is a "pedestrian", the radar device 1 sets the filter used in the filtering process to the particle filter which is a non-linear filter. This makes it possible to improve the trackability of "pedestrians" whose movement fluctuations are larger than those of "other vehicles" and "bicycles".

Next, a modification of the radar device 1 according to the above embodiment will be described.

In the above embodiment, the filter used in the filter processing is set for each type of target, but the present invention is not limited to this. The radar device 1 according to the modified example may set the same filter for a plurality of types of targets. For example, in the radar device 1 according to the modified example, when the target is "another vehicle", the filter used in the filter processing is set to the linear Kalman filter, and the target is "bicycle" and "pedestrian". , The filter used in the filtering process may be set to the particle filter.

In the above embodiment, filters having different types of filters are set so that the filter processing differs depending on the type of the target, but the present invention is not limited to this. The radar device 1 according to the modification may set a filter having different filter design parameters (parameter values) so that the filter processing differs depending on the type of the target. That is, the radar device 1 according to the modification is the same filter, and filters having different design parameters are set according to the type of the target.

For example, when the filter used in the filter processing is a linear Kalman filter regardless of the type of the target, the radar device 1 according to the modified example changes the drive noise covariance according to the type of the target. In the linear Kalman filter, the drive noise covariance, which represents the ambiguity of the motion model, is set. It is known that the linear Kalman filter can control the smoothness and traceability by changing the drive noise covariance. By changing this drive noise covariance according to the type of target, the linear Kalman filter can be used as a filter corresponding to the type of target.

For example, if the drive noise covariance is reduced, the traceability when the moving direction of the target is suddenly changed is lowered, but the variation in the target data can be reduced. Further, if the drive noise covariance is increased, the variation of the target data becomes large, but the traceability is improved even when the moving direction of the target changes suddenly.

Therefore, the drive noise covariance is made small for "cars" with small movement fluctuations, and the drive noise covariance is made large for "pedestrians" with large movement fluctuations. In this way, by setting the filter design parameters according to the target, the radar device 1 according to the modified example can generate target data for different types of targets using the same filter. can. That is, the radar device 1 according to the modified example can track different types of targets using the same filter.

In addition to the drive noise covariance, the design parameters of the linear Kalman filter include the initial value of the error covariance matrix, and the initial value of the error covariance matrix may be set according to the type of the target.

Further, for example, when the design parameters are changed and executed in the filter processing in which the linear Kalman filter is the same filter, in the case of a pedestrian, the weighting of the predicted value of this time based on the result detected in the previous processing is performed this time. Weighting may be smaller than the observed value that is the result detected by the process. Since pedestrians have large movement fluctuations and it may be difficult to predict the movement of the pedestrian, if the weighting of the predicted value this time based on the result detected in the previous processing is increased, the pedestrian will perform a different movement from the prediction. If this is the case, an erroneous detection result will be obtained. Therefore, the radar device 1 according to the modified example sets the weighting of the observed value, which is the result detected in this process, larger than the predicted value of this time (increasing the reliability), so that the target related to the pedestrian can be measured. Accurate detection results can be obtained.

Further, for example, when the particle filter is used as the same filter in the filter processing regardless of the type of the target, the number of particles and the variation of the particles are set according to the type of the target. For example, the number of particles is increased as the target becomes "another vehicle", "bicycle", or "pedestrian". This allows different types of targets to be tracked using the same filter.

Further, the radar device 1 according to the modified example may identify the type of the target based on the image data acquired by the camera.

Further, the radar device 1 according to the modified example may set a filter used in the filter processing according to the surrounding environment of the own vehicle MC. The radar device 1 according to the modified example sets a filter according to the surrounding environment detected by using, for example, a camera or a navigation system. The surrounding environment is, for example, the presence or absence of a pedestrian crossing or tunnel in front, and the type of road on which the own vehicle MC is traveling (general road, highway, school zone, etc.).

In the radar device 1 according to the modified example, for example, when the own vehicle MC is traveling on the highway, the filter used in the filter processing is set to the linear Kalman filter. Further, in the radar device 1 according to the modified example, for example, when the own vehicle MC is traveling on a general road, the filter used in the filter processing is set in the extended Kalman filter. Further, in the radar device 1 according to the modified example, for example, when the own vehicle MC is traveling in the school zone, the filter used in the filter processing is set in the particle filter. In this way, by setting the filter according to the surrounding environment of the own vehicle MC, it is possible to detect and track the target according to the surrounding environment.

The radar device 1 according to the modified example may set a filter used in the filter processing according to the time zone in which the own vehicle MC is traveling. In the radar device 1 according to the modified example, for example, when the own vehicle MC is traveling in the school zone and the time zone for going to and from school is set, the filter used for the filter processing is set in the particle filter, and in other time zones. Sets the filter used in the filtering process to the extended Kalman filter.

Further, the radar device 1 according to the modified example may change the filter used in the filter processing according to the number of detected targets. For example, in the radar device 1 according to the modified example, when the number of detected targets is small (for example, equal to or less than a predetermined threshold value), the filter used in the filter processing is set in the particle filter. This makes it possible to improve the traceability to the target.

Further, in the radar device 1 according to the modified example, when the number of detected targets is large (for example, more than a predetermined threshold value), the filter used in the filtering process is set to the linear Kalman filter. As a result, the processing load can be reduced when the number of targets is large.

The types of targets are not limited to "other vehicles," "bicycles," and "pedestrians." For example, as a type of further target, "motorcycle" may be included, and a filter corresponding to "motorcycle" may be set.

The radar device 1 according to the above embodiment and the radar device 1 according to the modified example may be applied in combination. That is, the radar device 1 sets a filter in which at least one of the filter type and the design parameter of the filter is different so that the filter processing differs depending on the type of the target, and the target data is generated by the set filter. This makes it possible to track different types of targets.

Further effects and variations can be easily derived by those skilled in the art. For this reason, the broader aspects of the invention are not limited to the particular details and representative embodiments described and described above. Thus, various modifications can be made without departing from the spirit or scope of the general concept of the invention as defined by the appended claims and their equivalents.

1 Radar device 30 Processing unit 32 Signal processing unit 32d Identification unit 32e Filter setting unit (setting unit)
32f filter processing unit (generation unit)
MC own vehicle

Claims (3)

An identification unit that identifies the type of target,
A setting unit that sets a filter in which at least one of the filter type and the parameter value of the filter is different so that the filter processing for generating the target data related to the target by time series filtering differs depending on the type of the target. ,
It is equipped with a generator that generates the target data using the set filter.
The setting unit is
If the target is a car, set the filter to a linear filter and
When the target is a bicycle, the filter is set to the Kalman filter, which is a non-linear filter .
A radar device characterized in that the filter is set to a particle filter when the target is a person. The setting unit is
The radar device according to claim 1, wherein the filter is set in which at least one of the type of the filter and the parameter value of the filter differs depending on the surrounding environment of the own vehicle. An identification process that identifies the type of target, and
A setting step in which a filter having at least one of a different type of filter and a parameter value of the filter is set so that the filtering process for generating target data related to the target by time series filtering differs depending on the type of the target. When,
Including a generation step in which target data related to the target is generated using the set filtering process.
In the setting process ,
If the target is a car, the filter is set to a linear filter and
When the target is a bicycle, the filter is set to the Kalman filter, which is a non-linear filter .
A target detection method, characterized in that the filter is set to a particle filter when the target is a person.

Images