JP2019200079A - Target detector and target detection method - Google Patents

Abstract

To provide a target detector and a target detection method which can improve the accuracy of detecting a target.SOLUTION: A target detector relating to an embodiment comprises a generation unit and a filter processing unit. The generation unit generates instantaneous data that corresponds to a target, on the basis of a reflected wave of a transmitted radio wave having been reflected by a target. The filter processing unit applies time-series filtering to the instantaneous data generated by the generation unit and thereby generates target data that corresponds to the target. The filter processing circuit includes an allocation unit for allocating latest instantaneous data to predictive data of the latest target data based on previous target data and thereby establishing continuity with the previous target data. The allocation unit is capable of allocating a plurality of predictive data to one of the latest instantaneous data.SELECTED DRAWING: Figure 2

Description

  The present invention relates to a target detection apparatus and a target detection method.

  2. Description of the Related Art Conventionally, for example, a target detection apparatus that detects a target based on a reflected wave obtained by transmitting a radio wave around the vehicle and reflecting the transmitted radio wave on the target is known. Some target detection devices generate target data by performing time-series filtering on instantaneous data that are reflection points of the target, for example (see, for example, Patent Document 1). Further, in such time series filtering, for example, the previous target data is assigned to the instantaneous data of this time, thereby obtaining continuity with the previous target data.

Japanese Patent Laid-Open No. 2015-210157

  However, the above-described conventional technology has room for further improvement in improving the target detection accuracy. Specifically, conventionally, since one target data is assigned to one instantaneous data, when the number of instantaneous data is smaller than the target data, there is a possibility that so-called instantaneous data may be exchanged with the target data. there were. If such an interaction occurs, replacement of target data may occur, and the reliability of target detection may be reduced.

  The present invention has been made in view of the above, and an object of the present invention is to provide a target detection device and a target detection method that can improve the detection accuracy of a target.

  In order to solve the above-described problems and achieve the object, a target detection apparatus according to the present invention includes a generation unit and a filter processing unit. The generation unit generates instantaneous data corresponding to the target based on a reflected wave of the transmitted radio wave reflected by the target. The filter processing unit generates target data corresponding to the target by performing time-series filtering on the instantaneous data generated by the generation unit. Further, the filter processing unit assigns the current instantaneous data to the prediction data of the current target data based on the previous target data, thereby obtaining continuity with the previous target data. An allocation unit is provided. The assigning unit can assign a plurality of the prediction data to one piece of the instantaneous data.

  According to the present invention, the target detection accuracy can be improved.

FIG. 1A is a diagram illustrating an outline of a target detection method according to an embodiment. FIG. 1B is a diagram illustrating an outline of the target detection method according to the embodiment. FIG. 2 is a block diagram illustrating a configuration of the radar apparatus according to the embodiment. FIG. 3 is an explanatory diagram of processing from the previous stage processing of the signal processing unit to the peak extraction processing in the signal processing unit. FIG. 4A is an explanatory diagram of angle estimation processing. FIG. 4B is a process explanatory diagram (part 1) of the pairing process. FIG. 4C is a process explanatory diagram (part 2) of the pairing process. FIG. 5 is a diagram illustrating a multiple allocation process by the allocation unit. FIG. 6 is a diagram illustrating a process for generating a representative value of target data. FIG. 7 is a flowchart illustrating a processing procedure of processing executed by the target detection device according to the embodiment. FIG. 8 is a flowchart illustrating a processing procedure of allocation processing executed by the allocation unit according to the embodiment. FIG. 9 is a flowchart illustrating a processing procedure of representative value generation processing executed by the target data generation unit.

  Hereinafter, embodiments of a target detection device and a target detection method disclosed in the present application will be described in detail with reference to the accompanying drawings. In addition, this invention is not limited by this embodiment. In the following, a case where the target detection device is an FM-CW (Frequency Modulated Continuous Wave) method will be described as an example, but the target detection device may be another example such as an FCM (Fast-Chirp Modulation) method. It may be a method.

  First, the outline | summary of the target detection method which concerns on embodiment is demonstrated using FIG. 1A and FIG. 1B. 1A and 1B are diagrams illustrating an overview of a target detection method according to an embodiment. FIG. 1A shows a host vehicle MC on which the target detection device 1 according to the embodiment is mounted, and an other vehicle LC positioned in front of the host vehicle MC.

  As illustrated in FIG. 1A, the target detection device 1 is mounted, for example, in a front grill of the host vehicle MC, and detects a target (for example, another vehicle LC) existing in the traveling direction of the host vehicle MC. In addition, the mounting location of the target detection apparatus 1 may be mounted in other locations such as a windshield, a rear grill, and left and right side portions (for example, left and right door mirrors).

  Further, as shown in FIG. 1A, the target detection device 1 generates instantaneous data 100 corresponding to the target based on the reflected wave reflected by the other vehicle LC from the radio wave transmitted around the host vehicle MC (step). S1). The instantaneous data 100 includes, for example, information such as the relative speed of the direction to the host vehicle MC, the distance between the host vehicle MC and the instantaneous data 100, and the angle of the instantaneous data 100.

  Moreover, the target detection apparatus 1 generates target data corresponding to the target by performing time-series filtering on the generated instantaneous data 100 (step S2). For the time series filtering, for example, a known time series filtering such as a particle filter or a Kalman filter can be adopted. In the following description, the time-series filtering will be described using a particle filter as an example.

  FIG. 1B shows processing for taking continuity in time series filtering. FIG. 1B shows target data in the previous cycle (hereinafter referred to as previous target data) 50a and 50b and predicted data in the current cycle predicted from previous target data 50a and 50b (hereinafter referred to as current prediction). Data) 60a, 60b, and instantaneous data (hereinafter referred to as current instantaneous data) 100 of the current cycle. Details of the prediction process for predicting the current prediction data 60a, 60b from the previous target data 50a, 50b will be described later.

  In time-series filtering, continuity with the previous target data 50a, 50 is obtained by assigning the prediction data 60a, 60b to the current instantaneous data 100.

  Here, a conventional target detection method will be described. In the conventional target detection method, one prediction data is assigned to one instantaneous data. For this reason, when the number of instantaneous data is smaller than the predicted data, there is a possibility that so-called instantaneous data may be exchanged with the predicted data. When such an interaction occurs, there is a risk that the prediction data assigned to the instantaneous data may be replaced, and the reliability of target detection may be reduced. Thus, conventionally, there has been room for further improvement in improving the target detection accuracy.

  Therefore, in the target detection method according to the embodiment, a plurality of prediction data 60 a and 60 b can be assigned to one instantaneous data 100. Specifically, the target detection device 1 according to the embodiment enables a plurality of pieces of prediction data 60a and 60b to be assigned to one current instantaneous data 100.

  And the target detection apparatus 1 which concerns on embodiment produces | generates the two target data of this time corresponding to each of the two prediction data 60a and 60b based on one allocated instantaneous data 100 of this time.

  As a result, the exchange of the prediction data 60a and 60b does not occur because the exchange of the instantaneous data 100 by the prediction data 60a and 60b does not occur. That is, according to the target detection method according to the embodiment, the detection accuracy of the target can be improved.

  In the example shown in FIG. 1B, the case where two prediction data 60a and 60b are assigned to one instantaneous data 100 is shown, but the number of prediction data 60a and 60b assigned to one instantaneous data 100 is three. There may be more than one.

  In the following description, when the plurality of prediction data 60a and 60b, the plurality of target data 50a and 50b, and the allocation ranges Ra and Rb are not distinguished, they are described as the prediction data 60, the target data 50, and the allocation range R, respectively. There is a case.

  Next, with reference to FIG. 2, the structure of the target detection apparatus 1 which concerns on embodiment is demonstrated in detail. FIG. 2 is a block diagram illustrating a configuration of the target detection device 1 according to the embodiment. In FIG. 2, only components necessary for explaining the features of the present embodiment are represented by functional blocks, and descriptions of general components are omitted.

  In other words, each component illustrated in FIG. 2 is functionally conceptual and does not necessarily need to be physically configured as illustrated. For example, the specific form of distribution / integration of each functional block is not limited to the one shown in the figure, and all or a part thereof is functionally or physically distributed in arbitrary units according to various loads or usage conditions.・ It can be integrated and configured.

  As illustrated in FIG. 2, the target detection device 1 includes a transmission unit 10, a reception unit 20, and a processing unit 30. The target detection device 1 is connected to a vehicle control device 2 that controls the behavior of the host vehicle MC.

  The vehicle control device 2 performs vehicle control such as PCS (Pre-crash Safety System) and AEB (Advanced Emergency Braking System) based on the detection result of the target by the target detection 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 modulation signal for transmitting a millimeter wave frequency-modulated with 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 transmission antenna 13 converts the transmission signal from the oscillator 12 into a transmission wave and outputs the transmission wave to the outside of the host vehicle MC. The transmission wave output from the transmission antenna 13 is a continuous wave that is frequency-modulated with a triangular wave. A transmission wave transmitted from the transmission antenna 13 to the outside of the host vehicle MC, for example, forward is reflected by a target such as the other vehicle LC to become a reflected wave.

  The receiving unit 20 includes a plurality of receiving antennas 21 that form 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 reception antenna 21.

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

  The reception signal output from the reception antenna 21 is amplified by an amplifier (not shown) (for example, a low noise amplifier) 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, generates a beat signal, and outputs the beat signal to the A / D conversion unit 23. .

  The beat signal is a differential wave between the transmission wave and the reflected wave, and is a frequency of the transmission signal (hereinafter referred to as “transmission frequency”) and a frequency of the reception signal (hereinafter referred to as “reception frequency”). It has a beat frequency that makes a difference. The beat signal generated by the mixer 22 is converted to 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 signal processing unit 32 includes a generation unit 32a and a filter processing unit 32b.

  The storage unit 33 stores history data 33a. The history data 33 a is information including the history of the target data 50 and the history of the instantaneous data 100 in a series of signal processing executed by the signal processing unit 32.

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

  The microcomputer CPU functions as the transmission / reception control unit 31 and the signal processing unit 32 by reading and executing the program stored in the ROM. Note that the transmission / reception control unit 31 and the signal processing unit 32 may all be configured by hardware such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA).

  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. Next, each component of the signal processing unit 32 will be described.

  The generation unit 32a generates instantaneous data 100. Specifically, the generation unit 32a generates instantaneous data 100 by performing frequency analysis processing, peak extraction processing, and instantaneous data generation processing.

  In the frequency analysis process, a fast Fourier transform (FFT) process (hereinafter referred to as “FFT process”) is performed on the beat signal input from each A / D conversion unit 23. The result of the FFT processing is a frequency spectrum of the beat signal, and a 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).

  In the peak extraction process, a peak frequency that is a peak in the result of the FFT process by the frequency analysis process is extracted. In the peak extraction process, the peak frequency is extracted for each of the “UP section” and “DN section” of the beat signal described later.

  In the instantaneous data generation process, an angle estimation process for calculating the arrival angle of the reflected wave corresponding to each peak frequency extracted in the peak extraction process and its power value is executed. Note that at the time of execution of the angle estimation process, the arrival angle is an angle at which the target is estimated to exist, and hence may be described as “estimated angle” below.

  Further, in the instantaneous data generation process, a pairing process for determining a correct combination of peak frequencies of the “UP section” and the “DN section” based on the calculation result of the calculated estimated angle and power value is executed.

  In the instantaneous data generation process, the distance of each target to the host vehicle MC and the relative speed of the direction to the host vehicle MC are calculated from the determined combination result. In the instantaneous data processing, the calculated estimated angle, distance, and relative speed of each target are output to the filter processing unit 32b as the instantaneous data 100 for the latest cycle (latest scan), and the history data 33a in the storage unit 33 is output. Remember as.

  In order to make the explanation easy to understand, the flow of processing from the previous stage processing of the signal processing unit 32 to the processing in the signal processing unit 32 is shown in FIGS. FIG. 3 is an explanatory diagram of processing from the previous stage processing of the signal processing unit 32 to the peak extraction processing in the generation unit 32a.

  FIG. 4A is an explanatory diagram of angle estimation processing. 4B and 4C are process explanatory diagrams (part 1) and (part 2) of the pairing process. Note that FIG. 3 is divided into three regions by two thick downward white arrows. In the following, each of these areas will be described as an upper stage, a middle stage, and a lower stage 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 is reflected by the target and arrives as a reflection wave, and is received by the reception antenna 21 at the reception signal fr (t ).

  At this time, as shown in the upper part of FIG. 3, the reception signal fr (t) is delayed by a time difference T with respect to the transmission signal fs (t) according to the distance between the host vehicle MC and the target. Due to the time difference T and the Doppler effect based on the relative speed of the host vehicle MC and the target, the beat signal has a frequency fup in the “UP section” in which the frequency increases and a frequency fdn in the “DN section” in which the frequency decreases. Is obtained as a repeated signal (see the middle of FIG. 3).

  The lower part of FIG. 3 schematically shows the result of FFT processing of the beat signal in the frequency analysis processing 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 regions on the “UP section” side and the “DN section” side are obtained. In the peak extraction process, a peak frequency that is a peak in the waveform is extracted.

  For example, in the case of the example shown in the lower part of FIG. 3, the peak extraction threshold 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.

  On the “DN section” side, the peaks Pd1 to Pd3 are also determined as peaks by the peak extraction threshold, and the peak frequencies fd1 to fd3 are extracted, respectively.

  Here, the reflected wave from a plurality of targets may be mixed in the frequency component of each peak frequency extracted by the peak extraction process. Therefore, in the instantaneous data generation process, an angle estimation process for calculating a direction for each peak frequency is performed, and the presence of a target corresponding to each peak frequency is analyzed.

  The azimuth calculation in the instantaneous data generation process can be performed using a known arrival direction estimation technique such as ESPRIT (Estimation of Signal Parameters via Rotational Invariance Techniques).

  FIG. 4A schematically shows the azimuth calculation result of the instantaneous data generation process. In the instantaneous data generation process, the estimated angles of the targets (respective reflection points) respectively corresponding to the peaks Pu1 to Pu3 are calculated from the peaks Pu1 to Pu3 of the azimuth calculation result. Moreover, the magnitude | size of each peak Pu1-Pu3 becomes a power value. In the instantaneous data generation process, as shown in FIG. 4B, the angle estimation process is performed for each of the “UP section” side and the “DN section” side.

  Then, in the instantaneous data generation process, a pairing process is performed for combining each peak having an estimated angle and a power value close to each other in the direction calculation result. From the combination result, in the instantaneous data generation process, the distance of each target (each reflection point) corresponding to each combination of peaks and the relative speed of the direction to the host vehicle MC are calculated.

  The distance can be calculated based on the relationship of “distance ∝ (fup + fdn)”. The relative speed can be calculated based on the relationship of “speed-up (fup−fdn)”. As a result, as shown in FIG. 4C, a pairing process result indicating the instantaneous data 100 of the estimated angle, distance, and relative speed of each reflection point RP with respect to the host vehicle MC is obtained.

  Returning to FIG. 2, the filter processing unit 32b will be described. As illustrated in FIG. 2, the filter processing unit 32b includes a prediction unit 321b, an allocation unit 322b, a weighting unit 323b, a resampling unit 324b, and a target data generation unit 325b.

  The filter processing unit 32b generates target data 50 corresponding to the instantaneous data 100 by applying a particle filter that assigns a predetermined number of particle data to the instantaneous data 100 generated by the generating unit 32a.

The prediction unit 321b performs a process of predicting sample points (particle data) in the particle filter. Probability Specifically, the prediction unit 321b includes the latest period time t, when the distribution X _t of the particle data at time t, based on the distribution state X _t-1 time t-1 of the previous cycle N pieces of particle data are arranged (sampled) based on the density function. That is, in the prediction process, the prediction unit 321b distributes the particle data from the particle data at time t−1 to a region where the instantaneous data 100 is likely to appear at time t.

  Note that the prediction unit 321b distributes particle data in a predetermined distribution state for the instantaneous data 100 corresponding to the new target because there is no particle data of the previous cycle.

  Further, the prediction unit 321 b generates prediction data 60 corresponding to the current target data 50 based on the previous target data 50. Specifically, the prediction unit 321b generates the prediction data 60 based on the actual movement direction of the previous target data 50 and the relative speed in the movement direction.

  The assigning unit 322b performs processing for assigning the instantaneous data 100 in the latest cycle to the latest particle data that is the prediction result of the predicting unit 321b. Specifically, the assigning unit 322b performs prediction data 60 and prediction data 60 on the current instantaneous data 100 existing within a predetermined assignment range R corresponding to the current prediction data 60 generated by the prediction unit 321b. Assign particle data corresponding to.

  In addition, when there is the instantaneous data 100 that does not exist in the allocation range R of any target data 50, the allocation unit 322b treats the instantaneous data 100 as a new target.

  The assigning unit 322b can assign a plurality of prediction data 60 to one instantaneous data 100 of this time. Here, the multiple assignment process will be specifically described with reference to FIG.

  FIG. 5 is a diagram illustrating a multiple assignment process by the assignment unit 322b. The upper part of FIG. 5 shows two prediction data 60a and 60b and four instantaneous data 100a to 100d. Of the four instantaneous data 100a to 100d, three instantaneous data 100a to 100c are within the allocation ranges Ra and Rb of the prediction data 60a and 60b, and one instantaneous data 100d is outside the allocation ranges Ra and Rb. Suppose that there is.

  Further, in the lower part of FIG. 5, the cost values for the combinations of the two prediction data 60a and 60b and the four instantaneous data 100a to 100d are shown. The cost value is an index calculated by the assigning unit 322b, and is an evaluation value obtained by evaluating the relationship between the prediction data 60 and the instantaneous data 100. Specifically, the cost value is calculated based on, for example, a distance to the host vehicle MC, an angle, a relative speed, and the like. In the example illustrated in FIG. 5, the cost value is set to be lower as the combination of the instantaneous data 100 and the predicted data 60 that are closer in distance, angle, and relative speed. Since the instantaneous data 100d is outside the allocation ranges Ra and Rb of the prediction data 60a and 60b, the cost value is set to the maximum value that is the upper limit.

  As shown in the lower part of FIG. 5, the assigning unit 322b first assigns the instantaneous data 100 to the predicted data 60 based on the first assignment condition based on the cost value. Specifically, the assigning unit 322b assigns the predicted data 60 having the lowest cost value for each instantaneous data 100 assuming that the first assignment condition is satisfied. In the example shown in the lower part of FIG. 5, the assigning unit 322b assigns the prediction data 60a to the three instantaneous data 100a to 100c according to the first assignment condition.

  That is, under the first assignment condition, none of the instantaneous data 100a to 100d is assigned to the prediction data 60b. In such a case, the assigning unit 322b includes the prediction data 60b that is not assigned to the instantaneous data 100a to 100c according to the first assignment condition within the prediction range Rb and the other prediction data 60a is assigned to the instantaneous data 100a to 100a. 100c is assigned redundantly according to the second assignment condition.

  Specifically, the allocating unit 322b allocates the instantaneous data 100a having the lowest cost value among the cost values of the prediction data 60b as the second allocation condition so as to be allocated in duplicate. That is, the assigning unit 322b assigns two prediction data 60a and 60b to one instantaneous data 100a.

  As described above, a plurality of assignment processes are performed according to the cost values considering the distance, angle, and relative speed between the instantaneous data 100 and the prediction data 60, so that the plurality of prediction data 60 is appropriately assigned to one instantaneous data 100. be able to. Moreover, the instantaneous data 100 can be allocated to all the prediction data 60 by performing the allocation process according to the second allocation condition for the prediction data 60 that is not allocated according to the first allocation condition.

  Returning to FIG. 2, the weighting unit 323b will be described. The weighting unit 323b weights each of the particle data corresponding to the current instantaneous data 100 and the current predicted data 60 that are in the allocation relationship by the allocation unit 322b.

  For example, the weighting unit 323b increases the weight of particles close to the current instantaneous data 100 among the current particle data, and decreases the weight of particles far from the current instantaneous data 100. Here, “near” and “far” indicate that the Mahalanobis distance is “near” and “far”.

  In addition, when a plurality of prediction data 60 is assigned to one instantaneous data 100, the weighting unit 323b weights each particle data of the plurality of prediction data 60 based on the one instantaneous data 100.

  Next, the resampling unit 324b rearranges (resamples) the particle data based on the weights of the respective particle data. Specifically, the resampling unit 324b moves the particle data having a small weight closer to the instantaneous data 100 (the weight is large). More specifically, the resampling unit 324b rearranges particle data having a weight less than a predetermined threshold to particle data having a weight greater than or equal to a predetermined threshold. Thereby, the target data 50 generated by the target data generation unit 325b described later can be brought closer to the instantaneous data 100 than the predicted data 60.

  Note that, when a plurality of prediction data 60 is assigned to one instantaneous data 100, the resampling unit 324b is based on the relative relationship between the instantaneous data 100 and each of the plurality of prediction data 60. Determine the threshold value. Specifically, the resampling unit 324 b determines a threshold based on the Mahalanobis distance between each of the plurality of prediction data 60 and the instantaneous data 100.

  For example, the resampling unit 324b lowers the above-described threshold for the prediction data 60 having a long Mahalanobis distance from the instantaneous data 100, while increasing the above-described threshold for the prediction data 60 having a short Mahalanobis distance. For example, the resampling unit 324b may determine how much the threshold is to be lowered (or increased) according to the difference in Mahalanobis distance between the plurality of prediction data 60.

  That is, the resampling unit 324b causes the target data 50 corresponding to the prediction data 60 having a short Mahalanobis distance to be closer to the instantaneous data 100, and the target data 50 corresponding to the prediction data 60 having a long Mahalanobis distance is used. , Making it difficult to access the instantaneous data 100.

  Thereby, for example, when the noise component included in the instantaneous data 100 is relatively large, all of the plurality of target data 50 generated by the target data generation unit 325b described later is close to the instantaneous data 100. Therefore, the continuity of the target data 50 can be prevented from becoming unstable.

  The target data generation unit 325b recalculates the probability density function based on the current particle data rearranged by the resampling unit 324b, and generates the target data 50 based on the centroid of the recalculated probability density function. To do. The target data generation unit 325b generates the target data 50 based on the center of gravity of the probability density function, but may generate the target data 50 based on the average of the probability density function, for example.

  Further, the target data generation unit 325b treats the instantaneous data 100 to which particle data has not been assigned as a new target and outputs it as target data as it is. That is. In the case of a new target, the target data generation unit 325b outputs the instantaneous data 100 = target data.

  In addition, when a plurality of prediction data 60 is assigned to one instantaneous data 100, the target data generation unit 325b generates target data 50 corresponding to each of the plurality of prediction data 60. Thereby, even when the number of prediction data 60 is larger than the instantaneous data 100, continuity can be obtained for all the prediction data 60.

  In addition, the target data generation unit 325b has a plurality of targets corresponding to each of the plurality of prediction data 60 when a plurality of prediction data 60 is assigned to one instantaneous data 100 and a predetermined condition is satisfied. For the data 50, a representative value of the target data 50 is generated. Then, the target data generation unit 325b outputs the representative value to the vehicle control device 2.

  Here, the generation process of the representative value of the target data 50 by the target data generation unit 325b will be described with reference to FIG. FIG. 6 is a diagram illustrating a process for generating a representative value of the target data 50.

  In FIG. 6, the target data 50 is generated at a time t corresponding to the latest cycle, a time t−1 immediately before the time t, and a time t-2 immediately before the time t−1. Show. FIG. 6 shows a case where one instantaneous data 100 is continuous with the same plurality of target data 50a, 50b (predicted data 60a, 60b) over the past two cycles. In addition, the corresponding time is shown in parentheses of each symbol. That is, the instantaneous data 100 (t) indicates that it is the instantaneous data 100 obtained at time t.

  As shown in FIG. 6, the target data generation unit 325b has a plurality of data at time t when the allocation relationship between the instantaneous data 100 and the two prediction data 60a and 60b is continuous in the period from time t-2 to time t. The representative value 50ab of the target data 50a, 50b is generated. For example, the target data generation unit 325b may set the average value of the plurality of target data 50a and 50b as the representative value 50ab, and may set one of the plurality of target data 50a and 50b as the representative value 50ab.

  That is, the target data generation unit 325b increases the probability count of the same target according to the number of consecutive periods of the allocation relationship between the instantaneous data 100 and the plurality of prediction data 60, and the probability count is a predetermined threshold value. In the case described above, the plurality of pieces of target data 50 to be generated are collected as one representative value 50ab, assuming that the plurality of pieces of prediction data 60 have a high probability of being derived from the same target. Thereby, it can avoid that the some target data 50 continues being produced | generated with respect to the same target.

  Note that the target data generation unit 325b may adjust the increase range of the probability count described above according to the state of the target data 50, for example. For example, the target data generation unit 325b does not increase or increase the probability count when the movement vectors (movement directions and relative speeds) of the plurality of prediction data 60 assigned to one instantaneous data 100 are different. Minimize the count. In other words, when the motion vectors of the plurality of prediction data 60 are different, there is a high possibility that they are different targets. Therefore, even if one instantaneous data 100 is shared, the representative value 50ab is not generated. Thereby, it is possible to prevent the representative value 50ab from being erroneously generated from the plurality of target data 50 derived from different targets.

  Note that the target data generation unit 325b may increase the probability count increase count than usual when the movement vectors of the plurality of prediction data 60 assigned to one instantaneous data 100 are similar to each other. . Thereby, it can detect as the same target at an earlier stage by collecting the plurality of target data 50 into the representative value 50ab at an earlier stage.

  In addition, for example, when the past positions of the plurality of target data 50 are separated by a predetermined distance or more, the target data generation unit 325b may increase the probability count even if one instantaneous data 100 is shared. Is negligible. Thereby, for example, when the single instantaneous data 100 is shared when different targets approach each other, the representative value 50ab can be prevented from being erroneously generated.

  Next, a processing procedure of processing executed by the target detection device 1 according to the embodiment will be described with reference to FIG. FIG. 7 is a flowchart illustrating a processing procedure of processing executed by the target detection device 1 according to the embodiment.

  As illustrated in FIG. 7, first, the generation unit 32a generates instantaneous data 100 corresponding to a target based on a reflected wave obtained by reflecting the transmitted radio wave on the target (step S101).

  Subsequently, the prediction unit 321b of the filter processing unit 32b predicts the current particle data based on the previous particle data and the prediction corresponding to the current target data 50 based on the previous target data 50. A prediction process for predicting the data 60 is performed (step S102). In addition, when the previous particle data does not exist, the filter processing unit 32b predicts the current particle data based on the relative speed of the previous instantaneous data 100.

  Subsequently, the assigning unit 322b assigns the current instantaneous data 100 to the current particle data (step S103). A specific processing procedure of the assigning unit 322b will be described later with reference to FIG.

  Subsequently, the assigning unit 322b determines the presence / absence of a new target based on the presence / absence of the instantaneous data 100 to which the current particle data is not assigned (step S104). When the instantaneous data 100 is a new target (step S104, Yes), the allocating unit 322b determines predetermined particle data (for example, initial state particle data) for the instantaneous data 100 corresponding to the new target. Is set (step S105).

  Subsequently, when the instantaneous data 100 is not a new target (No at Step S104), the weighting unit 323b weights each of the current particle data based on the instantaneous data 100 (Step S106).

  Subsequently, the resampling unit 324b performs resampling of the current particle data based on the weighting by the weighting unit 323b (step S107). Subsequently, the target data generation unit 325b updates the probability density function of the current resampled particle data, generates the target data 50 based on the probability density function (step S108), and ends the processing. . The generation processing of the representative value 50ab of the target data 50 by the target data generation unit 325b will be described later with reference to FIG.

  Next, with reference to FIG. 8, a processing procedure of allocation processing executed by the allocation unit 322b according to the embodiment will be described. FIG. 8 is a flowchart illustrating a processing procedure of allocation processing executed by the allocation unit 322b according to the embodiment.

  As illustrated in FIG. 8, first, the assigning unit 322b calculates a cost value with the instantaneous data 100 existing in the assignment range R for each prediction data 60 (step S201).

  Subsequently, the allocation unit 322b performs allocation processing based on the first allocation condition based on the calculated cost value (step S202). For example, the allocation unit 322b allocates, for each instantaneous data 100, the prediction data 60 having the lowest cost value as the prediction data 60 that satisfies the first allocation condition.

  Then, the assigning unit 322b determines whether there is the prediction data 60 that is not assigned according to the first assignment condition (step S203). When there is the prediction data 60 that cannot be allocated due to the first allocation condition (Yes at Step S203), the allocation unit 322b performs allocation according to the second allocation condition (Step S204), and ends the allocation process. .

  On the other hand, when there is no prediction data 60 that is not allocated according to the first allocation condition (No in step S203), the allocation unit 322b ends the allocation process because all the prediction data 60 has been allocated.

  Next, the processing procedure of the generation process of the representative value 50ab executed by the target data generation unit 325b according to the embodiment will be described using FIG. FIG. 9 is a flowchart illustrating a processing procedure of a generation process of the representative value 50ab executed by the target data generation unit 325b.

  As illustrated in FIG. 9, the target data generation unit 325b determines whether the allocation unit 322b has assigned a plurality of pieces of prediction data 60 to one instantaneous data 100 (step S301).

  When a plurality of prediction data 60 is assigned to one instantaneous data 100 (Yes in step S301), the target data generation unit 325b has the same combination of the instantaneous data 100 and the plurality of prediction data 60 that are assigned. The probability count indicating the probability of being a target is increased (step S302).

  Subsequently, the target data generation unit 325b determines whether or not the probability count is equal to or greater than a predetermined threshold (step S303). The target data generation unit 325b generates a representative value 50ab of the target data 50 (step S304) when the probability count is greater than or equal to a predetermined threshold (step S303, Yes), and ends the process.

  On the other hand, in step S301, when the target data generation unit 325b cannot allocate a plurality of prediction data 60 to one instantaneous data 100 (No in step S301), the target data corresponding to each prediction data 60 is obtained. 50 is generated (step S305), and the process is terminated.

  In step S303, if the probability count is less than the predetermined threshold (No in step S303), the target data generation unit 325b proceeds to step S305.

  As described above, the target detection device 1 according to the embodiment includes the generation unit 32a and the filter processing unit 32b. The generation unit 32a generates instantaneous data 100 corresponding to a target based on a reflected wave obtained by reflecting the transmitted radio wave on the target. The filter processing unit 32b generates target data 50 corresponding to the target by performing time series filtering on the instantaneous data 100 generated by the generation unit 32a. Further, the filter processing unit 32 b assigns the current instantaneous data 100 to the prediction data 60 of the current target data 50 based on the previous target data 50, thereby increasing the continuity with the previous target data 50. An assigning unit 322b. The assigning unit 322b can assign a plurality of prediction data 60 to one current instantaneous data 100. Thereby, the detection accuracy of the target can be improved.

  In the above-described embodiment, the target detection device 1 is provided in the vehicle. However, it goes without saying that the target detection device 1 may be provided in a moving body other than the vehicle, such as a ship or an aircraft.

  In the above-described embodiment, ESPRIT is given as an example of the direction-of-arrival estimation method used by the target detection apparatus 1, but is not limited thereto. For example, DBF (Digital Beam Forming), PRISM (Propagator method based on an Improved Spatial-smoothing Matrix), MUSIC (Multiple Signal Classification), or the like may be used.

  Further, in the above-described embodiment, the case where the filter processing unit 32b uses a particle filter has been described. However, the time-series filtering to be used is not limited to the particle filter, and examples thereof include a Kalman filter and an αβ filter. Time series filtering may be used.

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

1 Radar Device 32 Signal Processing Unit 32a Generation Unit 32b Filter Processing Unit 33 Storage Unit 33a History Data 50, 50a, 50b Target Data 60, 60a, 60b Prediction Data 100 Instantaneous Data 321b Prediction Unit 322b Allocation Unit 323b Weighting Unit 324b Resampling 325b Target data generation unit LC Other vehicle MC Own vehicle

Claims (6)

A generating unit that generates instantaneous data corresponding to the target based on a reflected wave of the transmitted radio wave reflected by the target;
A filter processing unit that generates target data corresponding to the target by performing time series filtering on the instantaneous data generated by the generating unit, and
The filter processing unit
By assigning the current data to the prediction data of the current target data based on the previous target data, the assigning unit for taking continuity with the previous target data is provided,
The assigning unit is
A target detection apparatus characterized in that a plurality of prediction data can be assigned to one instantaneous data of the current time. The filter processing unit
For each of the current instantaneous data within a predetermined allocation range corresponding to the prediction data, further comprising an index calculation unit that calculates an evaluation index that evaluates the relationship with the prediction data,
The assigning unit is
Based on the evaluation index calculated by the index calculation unit, the prediction data that is not allocated to the instantaneous data according to a first allocation condition is set to a second value relative to the instantaneous data to which the other prediction data is allocated. The target detection apparatus according to claim 1, wherein the target is duplicated according to the allocation condition of the target. The filter processing unit
The target data generation unit that generates the current target data corresponding to each of the plurality of prediction data to which the current instantaneous data is allocated by the allocation unit is further provided. 2. The target detection apparatus according to 2. The time series filtering is:
A particle filter that assigns a predetermined number of particle data to the instantaneous data;
The filter processing unit
A weighting unit that gives a predetermined weight to the predetermined number of particle data based on the instantaneous data;
A re-sampling unit that rearranges the particle data in which the weight given by the weighting unit is less than a predetermined threshold among the predetermined number of particle data; and
The resampling unit
When the plurality of prediction data is assigned to the one instantaneous data, the predetermined threshold is determined based on a relative relationship between the instantaneous data and each of the plurality of prediction data. The target detection apparatus according to any one of claims 1 to 3. The filter processing unit
When the one instantaneous data of this time is continuous with the same plurality of previous target data over a predetermined period in the past, the current target is obtained from the plurality of previous target data. The representative value of data is produced | generated. The target detection apparatus as described in any one of Claims 1-4 characterized by these. A generating step for generating instantaneous data corresponding to the target based on a reflected wave reflected by the target of the transmitted radio wave;
A filtering process step of generating target data corresponding to the target by applying time series filtering to the instantaneous data generated by the generating step,
The filtering process includes
Allocating the instantaneous data of this time to the prediction data of the target data of the current time based on the target data of the previous time, including an assigning step of taking continuity with the target data of the previous time,
The assigning step includes
A target detection method characterized in that a plurality of prediction data can be assigned to one instantaneous data of this time.

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